Tag Archives: Digital Charter Implementation Act 2022 (Bill C-27)

Unveiling the Neurotechnology Landscape: Scientific Advancements, Innovations and Major Trends—a UNESCO report

Launched on Thursday, July 13, 2023 during UNESCO’s (United Nations Educational, Scientific, and Cultural Organization) “Global dialogue on the ethics of neurotechnology,” is a report tying together the usual measures of national scientific supremacy (number of papers published and number of patents filed) with information on corporate investment in the field. Consequently, “Unveiling the Neurotechnology Landscape: Scientific Advancements, Innovations and Major Trends” by Daniel S. Hain, Roman Jurowetzki, Mariagrazia Squicciarini, and Lihui Xu provides better insight into the international neurotechnology scene than is sometimes found in these kinds of reports. By the way, the report is open access.

Here’s what I mean, from the report‘s short summary,

Since 2013, government investments in this field have exceeded $6 billion. Private investment has also seen significant growth, with annual funding experiencing a 22-fold increase from 2010 to 2020, reaching $7.3 billion and totaling $33.2 billion.

This investment has translated into a 35-fold growth in neuroscience publications between 2000-2021 and 20-fold growth in innovations between 2022-2020, as proxied by patents. However, not all are poised to benefit from such developments, as big divides emerge.

Over 80% of high-impact neuroscience publications are produced by only ten countries, while 70% of countries contributed fewer than 10 such papers over the period considered. Similarly, five countries only hold 87% of IP5 neurotech patents.

This report sheds light on the neurotechnology ecosystem, that is, what is being developed, where and by whom, and informs about how neurotechnology interacts with other technological trajectories, especially Artificial Intelligence [emphasis mine]. [p. 2]

The money aspect is eye-opening even when you already have your suspicions. Also, it’s not entirely unexpected to learn that only ten countries produce over 80% of the high impact neurotech papers and that only five countries hold 87% of the IP5 neurotech patents but it is stunning to see it in context. (If you’re not familiar with the term ‘IP5 patents’, scroll down in this post to the relevant subhead. Hint: It means the patent was filed in one of the top five jurisdictions; I’ll leave you to guess which ones those might be.)

“Since 2013 …” isn’t quite as informative as the authors may have hoped. I wish they had given a time frame for government investments similar to what they did for corporate investments (e.g., 2010 – 2020). Also, is the $6B (likely in USD) government investment cumulative or an estimated annual number? To sum up, I would have appreciated parallel structure and specificity.

Nitpicks aside, there’s some very good material intended for policy makers. On that note, some of the analysis is beyond me. I haven’t used anything even somewhat close to their analytical tools in years and years. This commentaries reflects my interests and a very rapid reading. One last thing, this is being written from a Canadian perspective. With those caveats in mind, here’s some of what I found.

A definition, social issues, country statistics, and more

There’s a definition for neurotechnology and a second mention of artificial intelligence being used in concert with neurotechnology. From the report‘s executive summary,

Neurotechnology consists of devices and procedures used to access, monitor, investigate, assess, manipulate, and/or emulate the structure and function of the neural systems of animals or human beings. It is poised to revolutionize our understanding of the brain and to unlock innovative solutions to treat a wide range of diseases and disorders.

Similarly to Artificial Intelligence (AI), and also due to its convergence with AI, neurotechnology may have profound societal and economic impact, beyond the medical realm. As neurotechnology directly relates to the brain, it triggers ethical considerations about fundamental aspects of human existence, including mental integrity, human dignity, personal identity, freedom of thought, autonomy, and privacy [emphases mine]. Its potential for enhancement purposes and its accessibility further amplifies its prospect social and societal implications.

The recent discussions held at UNESCO’s Executive Board further shows Member States’ desire to address the ethics and governance of neurotechnology through the elaboration of a new standard-setting instrument on the ethics of neurotechnology, to be adopted in 2025. To this end, it is important to explore the neurotechnology landscape, delineate its boundaries, key players, and trends, and shed light on neurotech’s scientific and technological developments. [p. 7]

Here’s how they sourced the data for the report,

The present report addresses such a need for evidence in support of policy making in
relation to neurotechnology by devising and implementing a novel methodology on data from scientific articles and patents:

● We detect topics over time and extract relevant keywords using a transformer-
based language models fine-tuned for scientific text. Publication data for the period
2000-2021 are sourced from the Scopus database and encompass journal articles
and conference proceedings in English. The 2,000 most cited publications per year
are further used in in-depth content analysis.
● Keywords are identified through Named Entity Recognition and used to generate
search queries for conducting a semantic search on patents’ titles and abstracts,
using another language model developed for patent text. This allows us to identify
patents associated with the identified neuroscience publications and their topics.
The patent data used in the present analysis are sourced from the European
Patent Office’s Worldwide Patent Statistical Database (PATSTAT). We consider
IP5 patents filed between 2000-2020 having an English language abstract and
exclude patents solely related to pharmaceuticals.

This approach allows mapping the advancements detailed in scientific literature to the technological applications contained in patent applications, allowing for an analysis of the linkages between science and technology. This almost fully automated novel approach allows repeating the analysis as neurotechnology evolves. [pp. 8-9[

Findings in bullet points,

Key stylized facts are:
● The field of neuroscience has witnessed a remarkable surge in the overall number
of publications since 2000, exhibiting a nearly 35-fold increase over the period
considered, reaching 1.2 million in 2021. The annual number of publications in
neuroscience has nearly tripled since 2000, exceeding 90,000 publications a year
in 2021. This increase became even more pronounced since 2019.
● The United States leads in terms of neuroscience publication output (40%),
followed by the United Kingdom (9%), Germany (7%), China (5%), Canada (4%),
Japan (4%), Italy (4%), France (4%), the Netherlands (3%), and Australia (3%).
These countries account for over 80% of neuroscience publications from 2000 to
● Big divides emerge, with 70% of countries in the world having less than 10 high-
impact neuroscience publications between 2000 to 2021.
● Specific neurotechnology-related research trends between 2000 and 2021 include:
○ An increase in Brain-Computer Interface (BCI) research around 2010,
maintaining a consistent presence ever since.
○ A significant surge in Epilepsy Detection research in 2017 and 2018,
reflecting the increased use of AI and machine learning in healthcare.
○ Consistent interest in Neuroimaging Analysis, which peaks around 2004,
likely because of its importance in brain activity and language
comprehension studies.
○ While peaking in 2016 and 2017, Deep Brain Stimulation (DBS) remains a
persistent area of research, underlining its potential in treating conditions
like Parkinson’s disease and essential tremor.
● Between 2000 and 2020, the total number of patent applications in this field
increased significantly, experiencing a 20-fold increase from less than 500 to over
12,000. In terms of annual figures, a consistent upward trend in neurotechnology-10
related patent applications emerges, with a notable doubling observed between
2015 and 2020.
• The United States account for nearly half of all worldwide patent applications (47%).
Other major contributors include South Korea (11%), China (10%), Japan (7%),
Germany (7%), and France (5%). These five countries together account for 87%
of IP5 neurotech patents applied between 2000 and 2020.
○ The United States has historically led the field, with a peak around 2010, a
decline towards 2015, and a recovery up to 2020.
○ South Korea emerged as a significant contributor after 1990, overtaking
Germany in the late 2000s to become the second-largest developer of
neurotechnology. By the late 2010s, South Korea’s annual neurotechnology
patent applications approximated those of the United States.
○ China exhibits a sharp increase in neurotechnology patent applications in
the mid-2010s, bringing it on par with the United States in terms of
application numbers.
● The United States ranks highest in both scientific publications and patents,
indicating their strong ability to transform knowledge into marketable inventions.
China, France, and Korea excel in leveraging knowledge to develop patented
innovations. Conversely, countries such as the United Kingdom, Germany, Italy,
Canada, Brazil, and Australia lag behind in effectively translating neurotech
knowledge into patentable innovations.
● In terms of patent quality measured by forward citations, the leading countries are
Germany, US, China, Japan, and Korea.
● A breakdown of patents by technology field reveals that Computer Technology is
the most important field in neurotechnology, exceeding Medical Technology,
Biotechnology, and Pharmaceuticals. The growing importance of algorithmic
applications, including neural computing techniques, also emerges by looking at
the increase in patent applications in these fields between 2015-2020. Compared
to the reference year, computer technologies-related patents in neurotech
increased by 355% and by 92% in medical technology.
● An analysis of the specialization patterns of the top-5 countries developing
neurotechnologies reveals that Germany has been specializing in chemistry-
related technology fields, whereas Asian countries, particularly South Korea and
China, focus on computer science and electrical engineering-related fields. The
United States exhibits a balanced configuration with specializations in both
chemistry and computer science-related fields.
● The entities – i.e. both companies and other institutions – leading worldwide
innovation in the neurotech space are: IBM (126 IP5 patents, US), Ping An
Technology (105 IP5 patents, CH), Fujitsu (78 IP5 patents, JP), Microsoft (76 IP511
patents, US)1, Samsung (72 IP5 patents, KR), Sony (69 IP5 patents JP) and Intel
(64 IP5 patents US)

This report further proposes a pioneering taxonomy of neurotechnologies based on International Patent Classification (IPC) codes.

• 67 distinct patent clusters in neurotechnology are identified, which mirror the diverse research and development landscape of the field. The 20 most prominent neurotechnology groups, particularly in areas like multimodal neuromodulation, seizure prediction, neuromorphic computing [emphasis mine], and brain-computer interfaces, point to potential strategic areas for research and commercialization.
• The variety of patent clusters identified mirrors the breadth of neurotechnology’s potential applications, from medical imaging and limb rehabilitation to sleep optimization and assistive exoskeletons.
• The development of a baseline IPC-based taxonomy for neurotechnology offers a structured framework that enriches our understanding of this technological space, and can facilitate research, development and analysis. The identified key groups mirror the interdisciplinary nature of neurotechnology and underscores the potential impact of neurotechnology, not only in healthcare but also in areas like information technology and biomaterials, with non-negligible effects over societies and economies.

1 If we consider Microsoft Technology Licensing LLM and Microsoft Corporation as being under the same umbrella, Microsoft leads worldwide developments with 127 IP5 patents. Similarly, if we were to consider that Siemens AG and Siemens Healthcare GmbH belong to the same conglomerate, Siemens would appear much higher in the ranking, in third position, with 84 IP5 patents. The distribution of intellectual property assets across companies belonging to the same conglomerate is frequent and mirrors strategic as well as operational needs and features, among others. [pp. 9-11]

Surprises and comments

Interesting and helpful to learn that “neurotechnology interacts with other technological trajectories, especially Artificial Intelligence;” this has changed and improved my understanding of neurotechnology.

It was unexpected to find Canada in the top ten countries producing neuroscience papers. However, finding out that the country lags in translating its ‘neuro’ knowledge into patentable innovation is not entirely a surprise.

It can’t be an accident that countries with major ‘electronics and computing’ companies lead in patents. These companies do have researchers but they also buy startups to acquire patents. They (and ‘patent trolls’) will also file patents preemptively. For the patent trolls, it’s a moneymaking proposition and for the large companies, it’s a way of protecting their own interests and/or (I imagine) forcing a sale.

The mention of neuromorphic (brainlike) computing in the taxonomy section was surprising and puzzling. Up to this point, I’ve thought of neuromorphic computing as a kind of alternative or addition to standard computing but the authors have blurred the lines as per UNESCO’s definition of neurotechnology (specifically, “… emulate the structure and function of the neural systems of animals or human beings”) . Again, this report is broadening my understanding of neurotechnology. Of course, it required two instances before I quite grasped it, the definition and the taxonomy.

What’s puzzling is that neuromorphic engineering, a broader term that includes neuromorphic computing, isn’t used or mentioned. (For an explanation of the terms neuromorphic computing and neuromorphic engineering, there’s my June 23, 2023 posting, “Neuromorphic engineering: an overview.” )

The report

I won’t have time for everything. Here are some of the highlights from my admittedly personal perspective.

It’s not only about curing disease

From the report,

Neurotechnology’s applications however extend well beyond medicine [emphasis mine], and span from research, to education, to the workplace, and even people’s everyday life. Neurotechnology-based solutions may enhance learning and skill acquisition and boost focus through brain stimulation techniques. For instance, early research finds that brain- zapping caps appear to boost memory for at least one month (Berkeley, 2022). This could one day be used at home to enhance memory functions [emphasis mine]. They can further enable new ways to interact with the many digital devices we use in everyday life, transforming the way we work, live and interact. One example is the Sound Awareness wristband developed by a Stanford team (Neosensory, 2022) which enables individuals to “hear” by converting sound into tactile feedback, so that sound impaired individuals can perceive spoken words through their skin. Takagi and Nishimoto (2023) analyzed the brain scans taken through Magnetic Resonance Imaging (MRI) as individuals were shown thousands of images. They then trained a generative AI tool called Stable Diffusion2 on the brain scan data of the study’s participants, thus creating images that roughly corresponded to the real images shown. While this does not correspond to reading the mind of people, at least not yet, and some limitations of the study have been highlighted (Parshall, 2023), it nevertheless represents an important step towards developing the capability to interface human thoughts with computers [emphasis mine], via brain data interpretation.

While the above examples may sound somewhat like science fiction, the recent uptake of generative Artificial Intelligence applications and of large language models such as ChatGPT or Bard, demonstrates that the seemingly impossible can quickly become an everyday reality. At present, anyone can purchase online electroencephalogram (EEG) devices for a few hundred dollars [emphasis mine], to measure the electrical activity of their brain for meditation, gaming, or other purposes. [pp. 14-15]

This is very impressive achievement. Some of the research cited was published earlier this year (2023). The extraordinary speed is a testament to the efforts by the authors and their teams. It’s also a testament to how quickly the field is moving.

I’m glad to see the mention of and focus on consumer neurotechnology. (While the authors don’t speculate, I am free to do so.) Consumer neurotechnology could be viewed as one of the steps toward normalizing a cyborg future for all of us. Yes, we have books, television programmes, movies, and video games, which all normalize the idea but the people depicted have been severely injured and require the augmentation. With consumer neurotechnology, you have easily accessible devices being used to enhance people who aren’t injured, they just want to be ‘better’.

This phrase seemed particularly striking “… an important step towards developing the capability to interface human thoughts with computers” in light of some claims made by the Australian military in my June 13, 2023 posting “Mind-controlled robots based on graphene: an Australian research story.” (My posting has an embedded video demonstrating the Brain Robotic Interface (BRI) in action. Also, see the paragraph below the video for my ‘measured’ response.)

There’s no mention of the military in the report which seems more like a deliberate rather than inadvertent omission given the importance of military innovation where technology is concerned.

This section gives a good overview of government initiatives (in the report it’s followed by a table of the programmes),

Thanks to the promises it holds, neurotechnology has garnered significant attention from both governments and the private sector and is considered by many as an investment priority. According to the International Brain Initiative (IBI), brain research funding has become increasingly important over the past ten years, leading to a rise in large-scale state-led programs aimed at advancing brain intervention technologies(International Brain Initiative, 2021). Since 2013, initiatives such as the United States’ Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative and the European Union’s Human Brain Project (HBP), as well as major national initiatives in China, Japan and South Korea have been launched with significant funding support from the respective governments. The Canadian Brain Research Strategy, initially operated as a multi- stakeholder coalition on brain research, is also actively seeking funding support from the government to transform itself into a national research initiative (Canadian Brain Research Strategy, 2022). A similar proposal is also seen in the case of the Australian Brain Alliance, calling for the establishment of an Australian Brain Initiative (Australian Academy of Science, n.d.). [pp. 15-16]


There are some concerns such as these,

Beyond the medical realm, research suggests that emotional responses of consumers
related to preferences and risks can be concurrently tracked by neurotechnology, such
as neuroimaging and that neural data can better predict market-level outcomes than
traditional behavioral data (Karmarkar and Yoon, 2016). As such, neural data is
increasingly sought after in the consumer market for purposes such as digital
phenotyping4, neurogaming 5,and neuromarketing6 (UNESCO, 2021). This surge in demand gives rise to risks like hacking, unauthorized data reuse, extraction of privacy-sensitive information, digital surveillance, criminal exploitation of data, and other forms of abuse. These risks prompt the question of whether neural data needs distinct definition and safeguarding measures.

These issues are particularly relevant today as a wide range of electroencephalogram (EEG) headsets that can be used at home are now available in consumer markets for purposes that range from meditation assistance to controlling electronic devices through the mind. Imagine an individual is using one of these devices to play a neurofeedback game, which records the person’s brain waves during the game. Without the person being aware, the system can also identify the patterns associated with an undiagnosed mental health condition, such as anxiety. If the game company sells this data to third parties, e.g. health insurance providers, this may lead to an increase of insurance fees based on undisclosed information. This hypothetical situation would represent a clear violation of mental privacy and of unethical use of neural data.

Another example is in the field of advertising, where companies are increasingly interested in using neuroimaging to better understand consumers’ responses to their products or advertisements, a practice known as neuromarketing. For instance, a company might use neural data to determine which advertisements elicit the most positive emotional responses in consumers. While this can help companies improve their marketing strategies, it raises significant concerns about mental privacy. Questions arise in relation to consumers being aware or not that their neural data is being used, and in the extent to which this can lead to manipulative advertising practices that unfairly exploit unconscious preferences. Such potential abuses underscore the need for explicit consent and rigorous data protection measures in the use of neurotechnology for neuromarketing purposes. [pp. 21-22]


Some countries already have laws and regulations regarding neurotechnology data,

At the national level, only a few countries have enacted laws and regulations to protect mental integrity or have included neuro-data in personal data protection laws (UNESCO, University of Milan-Bicocca (Italy) and State University of New York – Downstate Health Sciences University, 2023). Examples are the constitutional reform undertaken by Chile (Republic of Chile, 2021), the Charter for the responsible development of neurotechnologies of the Government of France (Government of France, 2022), and the Digital Rights Charter of the Government of Spain (Government of Spain, 2021). They propose different approaches to the regulation and protection of human rights in relation to neurotechnology. Countries such as the UK are also examining under which circumstances neural data may be considered as a special category of data under the general data protection framework (i.e. UK’s GDPR) (UK’s Information Commissioner’s Office, 2023) [p. 24]

As you can see, these are recent laws. There doesn’t seem to be any attempt here in Canada even though there is an act being reviewed in Parliament that could conceivably include neural data. This is from my May 1, 2023 posting,

Bill C-27 (Digital Charter Implementation Act, 2022) is what I believe is called an omnibus bill as it includes three different pieces of proposed legislation (the Consumer Privacy Protection Act [CPPA], the Artificial Intelligence and Data Act [AIDA], and the Personal Information and Data Protection Tribunal Act [PIDPTA]). [emphasis added July 11, 2023] You can read the Innovation, Science and Economic Development (ISED) Canada summary here or a detailed series of descriptions of the act here on the ISED’s Canada’s Digital Charter webpage.

My focus at the time was artificial intelligence and, now, after reading this UNESCO report and briefly looking at the Innovation, Science and Economic Development (ISED) Canada summary and a detailed series of descriptions of the act on ISED’s Canada’s Digital Charter webpage, I don’t see anything that specifies neural data but it’s not excluded either.

IP5 patents

Here’s the explanation (the footnote is included at the end of the excerpt),

IP5 patents represent a subset of overall patents filed worldwide, which have the
characteristic of having been filed in at least one top intellectual property offices (IPO)
worldwide (the so called IP5, namely the Chinese National Intellectual Property
Administration, CNIPA (formerly SIPO); the European Patent Office, EPO; the Japan
Patent Office, JPO; the Korean Intellectual Property Office, KIPO; and the United States
Patent and Trademark Office, USPTO) as well as another country, which may or may not be an IP5. This signals their potential applicability worldwide, as their inventiveness and industrial viability have been validated by at least two leading IPOs. This gives these patents a sort of “quality” check, also since patenting inventions is costly and if applicants try to protect the same invention in several parts of the world, this normally mirrors that the applicant has expectations about their importance and expected value. If we were to conduct the same analysis using information about individually considered patent applied worldwide, i.e. without filtering for quality nor considering patent families, we would risk conducting a biased analysis based on duplicated data. Also, as patentability standards vary across countries and IPOs, and what matters for patentability is the existence (or not) of prior art in the IPO considered, we would risk mixing real innovations with patents related to catching up phenomena in countries that are not at the forefront of the technology considered.

9 The five IP offices (IP5) is a forum of the five largest intellectual property offices in the world that was set up to improve the efficiency of the examination process for patents worldwide. The IP5 Offices together handle about 80% of the world’s patent applications, and 95% of all work carried out under the Patent Cooperation Treaty (PCT), see http://www.fiveipoffices.org. (Dernis et al., 2015) [p. 31]

AI assistance on this report

As noted earlier I have next to no experience with the analytical tools having not attempted this kind of work in several years. Here’s an example of what they were doing,

We utilize a combination of text embeddings based on Bidirectional Encoder
Representations from Transformer (BERT), dimensionality reduction, and hierarchical
clustering inspired by the BERTopic methodology 12 to identify latent themes within
research literature. Latent themes or topics in the context of topic modeling represent
clusters of words that frequently appear together within a collection of documents (Blei, 2012). These groupings are not explicitly labeled but are inferred through computational analysis examining patterns in word usage. These themes are ‘hidden’ within the text, only to be revealed through this analysis. …

We further utilize OpenAI’s GPT-4 model to enrich our understanding of topics’ keywords and to generate topic labels (OpenAI, 2023), thus supplementing expert review of the broad interdisciplinary corpus. Recently, GPT-4 has shown impressive results in medical contexts across various evaluations (Nori et al., 2023), making it a useful tool to enhance the information obtained from prior analysis stages, and to complement them. The automated process enhances the evaluation workflow, effectively emphasizing neuroscience themes pertinent to potential neurotechnology patents. Notwithstanding existing concerns about hallucinations (Lee, Bubeck and Petro, 2023) and errors in generative AI models, this methodology employs the GPT-4 model for summarization and interpretation tasks, which significantly mitigates the likelihood of hallucinations. Since the model is constrained to the context provided by the keyword collections, it limits the potential for fabricating information outside of the specified boundaries, thereby enhancing the accuracy and reliability of the output. [pp. 33-34]

I couldn’t resist adding the ChatGPT paragraph given all of the recent hoopla about it.

Multimodal neuromodulation and neuromorphic computing patents

I think this gives a pretty good indication of the activity on the patent front,

The largest, coherent topic, termed “multimodal neuromodulation,” comprises 535
patents detailing methodologies for deep or superficial brain stimulation designed to
address neurological and psychiatric ailments. These patented technologies interact with various points in neural circuits to induce either Long-Term Potentiation (LTP) or Long-Term Depression (LTD), offering treatment for conditions such as obsession, compulsion, anxiety, depression, Parkinson’s disease, and other movement disorders. The modalities encompass implanted deep-brain stimulators (DBS), Transcranial Magnetic Stimulation (TMS), and transcranial Direct Current Stimulation (tDCS). Among the most representative documents for this cluster are patents with titles: Electrical stimulation of structures within the brain or Systems and methods for enhancing or optimizing neural stimulation therapy for treating symptoms of Parkinson’s disease and or other movement disorders. [p.65]

Given my longstanding interest in memristors, which (I believe) have to a large extent helped to stimulate research into neuromorphic computing, this had to be included. Then, there was the brain-computer interfaces cluster,

A cluster identified as “Neuromorphic Computing” consists of 366 patents primarily
focused on devices designed to mimic human neural networks for efficient and adaptable computation. The principal elements of these inventions are resistive memory cells and artificial synapses. They exhibit properties similar to the neurons and synapses in biological brains, thus granting these devices the ability to learn and modulate responses based on rewards, akin to the adaptive cognitive capabilities of the human brain.

The primary technology classes associated with these patents fall under specific IPC
codes, representing the fields of neural network models, analog computers, and static
storage structures. Essentially, these classifications correspond to technologies that are key to the construction of computers and exhibit cognitive functions similar to human brain processes.

Examples for this cluster include neuromorphic processing devices that leverage
variations in resistance to store and process information, artificial synapses exhibiting
spike-timing dependent plasticity, and systems that allow event-driven learning and
reward modulation within neuromorphic computers.

In relation to neurotechnology as a whole, the “neuromorphic computing” cluster holds significant importance. It embodies the fusion of neuroscience and technology, thereby laying the basis for the development of adaptive and cognitive computational systems. Understanding this specific cluster provides a valuable insight into the progressing domain of neurotechnology, promising potential advancements across diverse fields, including artificial intelligence and healthcare.

The “Brain-Computer Interfaces” cluster, consisting of 146 patents, embodies a key aspect of neurotechnology that focuses on improving the interface between the brain and external devices. The technology classification codes associated with these patents primarily refer to methods or devices for treatment or protection of eyes and ears, devices for introducing media into, or onto, the body, and electric communication techniques, which are foundational elements of brain-computer interface (BCI) technologies.

Key patents within this cluster include a brain-computer interface apparatus adaptable to use environment and method of operating thereof, a double closed circuit brain-machine interface system, and an apparatus and method of brain-computer interface for device controlling based on brain signal. These inventions mainly revolve around the concept of using brain signals to control external devices, such as robotic arms, and improving the classification performance of these interfaces, even after long periods of non-use.

The inventions described in these patents improve the accuracy of device control, maintain performance over time, and accommodate multiple commands, thus significantly enhancing the functionality of BCIs.

Other identified technologies include systems for medical image analysis, limb rehabilitation, tinnitus treatment, sleep optimization, assistive exoskeletons, and advanced imaging techniques, among others. [pp. 66-67]

Having sections on neuromorphic computing and brain-computer interface patents in immediate proximity led to more speculation on my part. Imagine how much easier it would be to initiate a BCI connection if it’s powered with a neuromorphic (brainlike) computer/device. [ETA July 21, 2023: Following on from that thought, it might be more than just easier to initiate a BCI connection. Could a brainlike computer become part of your brain? Why not? it’s been successfully argued that a robotic wheelchair was part of someone’s body, see my January 30, 2013 posting and scroll down about 40% of the way.)]

Neurotech policy debates

The report concludes with this,

Neurotechnology is a complex and rapidly evolving technological paradigm whose
trajectories have the power to shape people’s identity, autonomy, privacy, sentiments,
behaviors and overall well-being, i.e. the very essence of what it means to be human.

Designing and implementing careful and effective norms and regulations ensuring that neurotechnology is developed and deployed in an ethical manner, for the good of
individuals and for society as a whole, call for a careful identification and characterization of the issues at stake. This entails shedding light on the whole neurotechnology ecosystem, that is what is being developed, where and by whom, and also understanding how neurotechnology interacts with other developments and technological trajectories, especially AI. Failing to do so may result in ineffective (at best) or distorted policies and policy decisions, which may harm human rights and human dignity.

Addressing the need for evidence in support of policy making, the present report offers first time robust data and analysis shedding light on the neurotechnology landscape worldwide. To this end, its proposes and implements an innovative approach that leverages artificial intelligence and deep learning on data from scientific publications and paten[t]s to identify scientific and technological developments in the neurotech space. The methodology proposed represents a scientific advance in itself, as it constitutes a quasi- automated replicable strategy for the detection and documentation of neurotechnology- related breakthroughs in science and innovation, to be repeated over time to account for the evolution of the sector. Leveraging this approach, the report further proposes an IPC-based taxonomy for neurotechnology which allows for a structured framework to the exploration of neurotechnology, to enable future research, development and analysis. The innovative methodology proposed is very flexible and can in fact be leveraged to investigate different emerging technologies, as they arise.

In terms of technological trajectories, we uncover a shift in the neurotechnology industry, with greater emphasis being put on computer and medical technologies in recent years, compared to traditionally dominant trajectories related to biotechnology and pharmaceuticals. This shift warrants close attention from policymakers, and calls for attention in relation to the latest (converging) developments in the field, especially AI and related methods and applications and neurotechnology.

This is all the more important and the observed growth and specialization patterns are unfolding in the context of regulatory environments that, generally, are either not existent or not fit for purpose. Given the sheer implications and impact of neurotechnology on the very essence of human beings, this lack of regulation poses key challenges related to the possible infringement of mental integrity, human dignity, personal identity, privacy, freedom of thought, and autonomy, among others. Furthermore, issues surrounding accessibility and the potential for neurotech enhancement applications triggers significant concerns, with far-reaching implications for individuals and societies. [pp. 72-73]

Last words about the report

Informative, readable, and thought-provoking. And, it helped broaden my understanding of neurotechnology.

Future endeavours?

I’m hopeful that one of these days one of these groups (UNESCO, Canadian Science Policy Centre, or ???) will tackle the issue of business bankruptcy in the neurotechnology sector. It has already occurred as noted in my ““Going blind when your neural implant company flirts with bankruptcy [long read]” April 5, 2022 posting. That story opens with a woman going blind in a New York subway when her neural implant fails. It’s how she found out the company, which supplied her implant was going out of business.

In my July 7, 2023 posting about the UNESCO July 2023 dialogue on neurotechnology, I’ve included information on Neuralink (one of Elon Musk’s companies) and its approval (despite some investigations) by the US Food and Drug Administration to start human clinical trials. Scroll down about 75% of the way to the “Food for thought” subhead where you will find stories about allegations made against Neuralink.

The end

If you want to know more about the field, the report offers a seven-page bibliography and there’s a lot of material here where you can start with this December 3, 2019 posting “Neural and technological inequalities” which features an article mentioning a discussion between two scientists. Surprisingly (to me), the source article is in Fast Company (a leading progressive business media brand), according to their tagline)..

I have two categories you may want to check: Human Enhancement and Neuromorphic Engineering. There are also a number of tags: neuromorphic computing, machine/flesh, brainlike computing, cyborgs, neural implants, neuroprosthetics, memristors, and more.

Should you have any observations or corrections, please feel free to leave them in the Comments section of this posting.

Canada, AI regulation, and the second reading of the Digital Charter Implementation Act, 2022 (Bill C-27)

Bill C-27 (Digital Charter Implementation Act, 2022) is what I believe is called an omnibus bill as it includes three different pieces of proposed legislation (the Consumer Privacy Protection Act [CPPA], the Artificial Intelligence and Data Act [AIDA], and the Personal Information and Data Protection Tribunal Act [PIDPTA]). You can read the Innovation, Science and Economic Development (ISED) Canada summary here or a detailed series of descriptions of the act here on the ISED’s Canada’s Digital Charter webpage.

Months after the first reading in June 2022, Bill C-27 was mentioned here in a September 15, 2022 posting about a Canadian Science Policy Centre (CSPC) event featuring a panel discussion about the proposed legislation, artificial intelligence in particular. I dug down and found commentaries and additional information about the proposed bill with special attention to AIDA.

it seems discussion has been reactivated since the second reading was completed on April 24, 2023 and referred to committee for further discussion. (A report and third reading are still to be had in the House of Commons and then, there are three readings in the Senate before this legislation can be passed.)

Christian Paas-Lang has written an April 24, 2023 article for CBC (Canadian Broadcasting Corporation) news online that highlights concerns centred on AI from three cross-party Members of Parliament (MPs),

Once the domain of a relatively select group of tech workers, academics and science fiction enthusiasts, the debate over the future of artificial intelligence has been thrust into the mainstream. And a group of cross-party MPs say Canada isn’t yet ready to take on the challenge.

The popularization of AI as a subject of concern has been accelerated by the introduction of ChatGPT, an AI chatbot produced by OpenAI that is capable of generating a broad array of text, code and other content. ChatGPT relies on content published on the internet as well as training from its users to improve its responses.

ChatGPT has prompted such a fervour, said Katrina Ingram, founder of the group Ethically Aligned AI, because of its novelty and effectiveness. 

“I would argue that we’ve had AI enabled infrastructure or technologies around for quite a while now, but we haven’t really necessarily been confronted with them, you know, face to face,” she told CBC Radio’s The House [radio segment embedded in article] in an interview that aired Saturday [April 22, 2023].

Ingram said the technology has prompted a series of concerns: about the livelihoods of professionals like artists and writers, about privacy, data collection and surveillance and about whether chatbots like ChatGPT can be used as tools for disinformation.

With the popularization of AI as an issue has come a similar increase in concern about regulation, and Ingram says governments must act now.

“We are contending with these technologies right now. So it’s really imperative that governments are able to pick up the pace,” she told host Catherine Cullen.

That sentiment — the need for speed — is one shared by three MPs from across party lines who are watching the development of the AI issue. Conservative MP Michelle Rempel Garner, NDP MP Brian Masse and Nathaniel Erskine-Smith of the Liberals also joined The House for an interview that aired Saturday.

“This is huge. This is the new oil,” said Masse, the NDP’s industry critic, referring to how oil had fundamentally shifted economic and geopolitical relationships, leading to a great deal of good but also disasters — and AI could do the same.

Issues of both speed and substance

The three MPs are closely watching Bill C-27, a piece of legislation currently being debated in the House of Commons that includes Canada’s first federal regulations on AI.

But each MP expressed concern that the bill may not be ready in time and changes would be needed [emphasis mine].

“This legislation was tabled in June of last year [2022], six months before ChatGPT was released and it’s like it’s obsolete. It’s like putting in place a framework to regulate scribes four months after the printing press came out,” Rempel Garner said. She added that it was wrongheaded to move the discussion of AI away from Parliament and segment it off to a regulatory body.

Am I the only person who sees a problem with the “bill may not be ready in time and changes would be needed?” I don’t understand the rush (or how these people get elected). The point of a bill is to examine the ideas and make changes to it before it becomes legislation. Given how fluid the situation appears to be, a strong argument can be made for the current process which is three readings in the House of Commons, along with a committee report, and three readings in the senate before a bill, if successful, is passed into legislation.

Of course, the fluidity of the situation could also be an argument for starting over as Michael Geist’s (Canada Research Chair in Internet and E-Commerce Law at the University of Ottawa and member of the Centre for Law, Technology and Society) April 19, 2023 post on his eponymous blog suggests, Note: Links have been removed,

As anyone who has tried ChatGPT will know, at the bottom of each response is an option to ask the AI system to “regenerate response”. Despite increasing pressure on the government to move ahead with Bill C-27’s Artificial Intelligence and Data Act (AIDA), the right response would be to hit the regenerate button and start over. AIDA may be well-meaning and the issue of AI regulation critically important, but the bill is limited in principles and severely lacking in detail, leaving virtually all of the heavy lifting to a regulation-making process that will take years to unfold. While no one should doubt the importance of AI regulation, Canadians deserve better than virtue signalling on the issue with a bill that never received a full public consultation.

What prompts this post is a public letter based out of MILA that calls on the government to urgently move ahead with the bill signed by some of Canada’s leading AI experts. The letter states: …

When the signatories to the letter suggest that there is prospect of moving AIDA forward before the summer, it feels like a ChatGPT error. There are a maximum of 43 days left on the House of Commons calendar until the summer. In all likelihood, it will be less than that. Bill C-27 is really three bills in one: major privacy reform, the creation of a new privacy tribunal, and AI regulation. I’ve watched the progress of enough bills to know that this just isn’t enough time to conduct extensive hearings on the bill, conduct a full clause-by-clause review, debate and vote in the House, and then conduct another review in the Senate. At best, Bill C-27 could make some headway at committee, but getting it passed with a proper review is unrealistic.

Moreover, I am deeply concerned about a Parliamentary process that could lump together these three bills in an expedited process. …

For anyone unfamiliar with MILA, it is also known as Quebec’s Artificial Intelligence Institute. (They seem to have replaced institute with ecosystem since the last time I checked.) You can see the document and list of signatories here.

Geist has a number of posts and podcasts focused on the bill and the easiest way to find them is to use the search term ‘Bill C-27’.

Maggie Arai at the University of Toronto’s Schwartz Reisman Institute for Technology and Society provides a brief overview titled, Five things to know about Bill C-27, in her April 18, 2022 commentary,

On June 16, 2022, the Canadian federal government introduced Bill C-27, the Digital Charter Implementation Act 2022, in the House of Commons. Bill C-27 is not entirely new, following in the footsteps of Bill C-11 (the Digital Charter Implementation Act 2020). Bill C-11 failed to pass, dying on the Order Paper when the Governor General dissolved Parliament to hold the 2021 federal election. While some aspects of C-27 will likely be familiar to those who followed the progress of Bill C-11, there are several key differences.

After noting the differences, Arai had this to say, from her April 18, 2022 commentary,

The tabling of Bill C-27 represents an exciting step forward for Canada as it attempts to forge a path towards regulating AI that will promote innovation of this advanced technology, while simultaneously offering consumers assurance and protection from the unique risks this new technology it poses. This second attempt towards the CPPA and PIDPTA is similarly positive, and addresses the need for updated and increased consumer protection, privacy, and data legislation.

However, as the saying goes, the devil is in the details. As we have outlined, several aspects of how Bill C-27 will be implemented are yet to be defined, and how the legislation will interact with existing social, economic, and legal dynamics also remains to be seen.

There are also sections of C-27 that could be improved, including areas where policymakers could benefit from the insights of researchers with domain expertise in areas such as data privacy, trusted computing, platform governance, and the social impacts of new technologies. In the coming weeks, the Schwartz Reisman Institute will present additional commentaries from our community that explore the implications of C-27 for Canadians when it comes to privacy, protection against harms, and technological governance.

Bryan Short’s September 14, 2022 posting (The Absolute Bare Minimum: Privacy and the New Bill C-27) on the Open Media website critiques two of the three bills included in Bill C-27, Note: Links have been removed,

The Canadian government has taken the first step towards creating new privacy rights for people in Canada. After a failed attempt in 2020 and three years of inaction since the proposal of the digital charter, the government has tabled another piece of legislation aimed at giving people in Canada the privacy rights they deserve.

In this post, we’ll explore how Bill C-27 compares to Canada’s current privacy legislation, how it stacks up against our international peers, and what it means for you. This post considers two of the three acts being proposed in Bill C-27, the Consumer Privacy Protection Act (CPPA) and the Personal Information and Data Tribunal Act (PIDTA), and doesn’t discuss the Artificial Intelligence and Data Act [emphasis mine]. The latter Act’s engagement with very new and complex issues means we think it deserves its own consideration separate from existing privacy proposals, and will handle it as such.

If we were to give Bill C-27’s CPPA and PIDTA a grade, it’d be a D. This is legislation that does the absolute bare minimum for privacy protections in Canada, and in some cases it will make things actually worse. If they were proposed and passed a decade ago, we might have rated it higher. However, looking ahead at predictable movement in data practices over the next ten – or even twenty – years, these laws will be out of date the moment they are passed, and leave people in Canada vulnerable to a wide range of predatory data practices. For detailed analysis, read on – but if you’re ready to raise your voice, go check out our action calling for positive change before C-27 passes!

Taking this all into account, Bill C-27 isn’t yet the step forward for privacy in Canada that we need. While it’s an improvement upon the last privacy bill that the government put forward, it misses so many areas that are critical for improvement, like failing to put people in Canada above the commercial interests of companies.

If Open Media has followed up with an AIDA critique, I have not been able to find it on their website.

Age of AI and Big Data – Impact on Justice, Human Rights and Privacy Zoom event on September 28, 2022 at 12 – 1:30 pm EDT

The Canadian Science Policy Centre (CSPC) in a September 15, 2022 announcement (received via email) announced an event (Age of AI and Big Data – Impact on Justice, Human Rights and Privacy) centered on some of the latest government doings on artificial intelligence and privacy (Bill C-27),

In an increasingly connected world, we share a large amount of our data in our daily lives without our knowledge while browsing online, traveling, shopping, etc. More and more companies are collecting our data and using it to create algorithms or AI. The use of our data against us is becoming more and more common. The algorithms used may often be discriminatory against racial minorities and marginalized people.

As technology moves at a high pace, we have started to incorporate many of these technologies into our daily lives without understanding its consequences. These technologies have enormous impacts on our very own identity and collectively on civil society and democracy. 

Recently, the Canadian Government introduced the Artificial Intelligence and Data Act (AIDA) and Bill C-27 [which includes three acts in total] in parliament regulating the use of AI in our society. In this panel, we will discuss how our AI and Big data is affecting us and its impact on society, and how the new regulations affect us. 

Date: Sep 28 Time: 12:00 pm – 1:30 pm EDT Event Category: Virtual Session

Register Here

For some reason, there was no information about the moderator and panelists, other than their names, titles, and affiliations. Here’s a bit more:

Moderator: Yuan Stevens (from her eponymous website’s About page), Note: Links have been removed,

Yuan (“You-anne”) Stevens (she/they) is a legal and policy expert focused on sociotechnical security and human rights.

She works towards a world where powerful actors—and the systems they build—are held accountable to the public, especially when it comes to marginalized communities. 

She brings years of international experience to her role at the Leadership Lab at Toronto Metropolitan University [formerly Ryerson University], having examined the impacts of technology on vulnerable populations in Canada, the US and Germany. 

Committed to publicly accessible legal and technical knowledge, Yuan has written for popular media outlets such as the Toronto Star and Ottawa Citizen and has been quoted in news stories by the New York Times, the CBC and the Globe & Mail.

Yuan is a research fellow at the Centre for Law, Technology and Society at the University of Ottawa and a research affiliate at Data & Society Research Institute. She previously worked at Harvard University’s Berkman Klein Center for Internet & Society during her studies in law at McGill University.

She has been conducting research on artificial intelligence since 2017 and is currently exploring sociotechnical security as an LL.M candidate at University of Ottawa’s Faculty of Law working under Florian Martin-Bariteau.

Panelist: Brenda McPhail (from her Centre for International Governance Innovation profile page),

Brenda McPhail is the director of the Canadian Civil Liberties Association’s Privacy, Surveillance and Technology Project. Her recent work includes guiding the Canadian Civil Liberties Association’s interventions in key court cases that raise privacy issues, most recently at the Supreme Court of Canada in R v. Marakah and R v. Jones, which focused on privacy rights in sent text messages; research into surveillance of dissent, government information sharing, digital surveillance capabilities and privacy in relation to emergent technologies; and developing resources and presentations to drive public awareness about the importance of privacy as a social good.

Panelist: Nidhi Hegde (from her University of Alberta profile page),

My research has spanned many areas such as resource allocation in networking, smart grids, social information networks, machine learning. Broadly, my interest lies in gaining a fundamental understanding of a given system and the design of robust algorithms.

More recently my research focus has been in privacy in machine learning. I’m interested in understanding how robust machine learning methods are to perturbation, and privacy and fairness constraints, with the goal of designing practical algorithms that achieve privacy and fairness.


Before joining the University of Alberta, I spent many years in industry research labs. Most recently, I was a Research team lead at Borealis AI (a research institute at Royal Bank of Canada), where my team worked on privacy-preserving methods for machine learning models and other applied problems for RBC. Prior to that, I spent many years in research labs in Europe working on a variety of interesting and impactful problems. I was a researcher at Bell Labs, Nokia, in France from January 2015 to March 2018, where I led a new team focussed on Maths and Algorithms for Machine Learning in Networks and Systems, in the Maths and Algorithms group of Bell Labs. I also spent a few years at the Technicolor Paris Research Lab working on social network analysis, smart grids, and privacy in recommendations.

Panelist: Benjamin Faveri (from his LinkedIn page),


Benjamin Faveri is a Research and Policy Analyst at the Responsible AI Institute (RAII) [headquarted in Austin, Texas]. Currently, he is developing their Responsible AI Certification Program and leading it through Canada’s national accreditation process. Over the last several years, he has worked on numerous certification program-related research projects such as fishery economics and certification programs, police body-worn camera policy certification, and emerging AI certifications and assurance systems. Before his work at RAII, Benjamin completed a Master of Public Policy and Administration at Carleton University, where he was a Canada Graduate Scholar, Ontario Graduate Scholar, Social Innovation Fellow, and Visiting Scholar at UC Davis School of Law. He holds undergraduate degrees in criminology and psychology, finishing both with first class standing. Outside of work, Benjamin reads about how and why certification and private governance have been applied across various industries.

Panelist: Ori Freiman (from his eponymous website’s About page)

I research at the forefront of technological innovation. This website documents some of my academic activities.

My formal background is in Analytic Philosophy, Library and Information Science, and Science & Technology Studies. Until September 22′ [September 2022], I was a Post-Doctoral Fellow at the Ethics of AI Lab, at the University of Toronto’s Centre for Ethics. Before joining the Centre, I submitted my dissertation, about trust in technology, to The Graduate Program in Science, Technology and Society at Bar-Ilan University.

I have also found a number of overviews and bits of commentary about the Canadian federal government’s proposed Bill C-27, which I think of as an omnibus bill as it includes three proposed Acts.

The lawyers are excited but I’m starting with the Responsible AI Institute’s (RAII) response first as one of the panelists (Benjamin Faveri) works for them and it’s a view from a closely neighbouring country, from a June 22, 2022 RAII news release, Note: Links have been removed,

Business Implications of Canada’s Draft AI and Data Act

On June 16 [2022], the Government of Canada introduced the Artificial Intelligence and Data Act (AIDA), as part of the broader Digital Charter Implementation Act 2022 (Bill C-27). Shortly thereafter, it also launched the second phase of the Pan-Canadian Artificial Intelligence Strategy.

Both RAII’s Certification Program, which is currently under review by the Standards Council of Canada, and the proposed AIDA legislation adopt the same approach of gauging an AI system’s risk level in context; identifying, assessing, and mitigating risks both pre-deployment and on an ongoing basis; and pursuing objectives such as safety, fairness, consumer protection, and plain-language notification and explanation.

Businesses should monitor the progress of Bill C-27 and align their AI governance processes, policies, and controls to its requirements. Businesses participating in RAII’s Certification Program will already be aware of requirements, such as internal Algorithmic Impact Assessments to gauge risk level and Responsible AI Management Plans for each AI system, which include system documentation, mitigation measures, monitoring requirements, and internal approvals.

The AIDA draft is focused on the impact of any “high-impact system”. Companies would need to assess whether their AI systems are high-impact; identify, assess, and mitigate potential harms and biases flowing from high-impact systems; and “publish on a publicly available website a plain-language description of the system” if making a high-impact system available for use. The government elaborated in a press briefing that it will describe in future regulations the classes of AI systems that may have high impact.

The AIDA draft also outlines clear criminal penalties for entities which, in their AI efforts, possess or use unlawfully obtained personal information or knowingly make available for use an AI system that causes serious harm or defrauds the public and causes substantial economic loss to an individual.

If enacted, AIDA would establish the Office of the AI and Data Commissioner, to support Canada’s Minister of Innovation, Science and Economic Development, with powers to monitor company compliance with the AIDA, to order independent audits of companies’ AI activities, and to register compliance orders with courts. The Commissioner would also help the Minister ensure that standards for AI systems are aligned with international standards.

Apart from being aligned with the approach and requirements of Canada’s proposed AIDA legislation, RAII is also playing a key role in the Standards Council of Canada’s AI  accreditation pilot. The second phase of the Pan-Canadian includes funding for the Standards Council of Canada to “advance the development and adoption of standards and a conformity assessment program related to AI/”

The AIDA’s introduction shows that while Canada is serious about governing AI systems, its approach to AI governance is flexible and designed to evolve as the landscape changes.

Charles Mandel’s June 16, 2022 article for Betakit (Canadian Startup News and Tech Innovation) provides an overview of the government’s overall approach to data privacy, AI, and more,

The federal Liberal government has taken another crack at legislating privacy with the introduction of Bill C-27 in the House of Commons.

Among the bill’s highlights are new protections for minors as well as Canada’s first law regulating the development and deployment of high-impact AI systems.

“It [Bill C-27] will address broader concerns that have been expressed since the tabling of a previous proposal, which did not become law,” a government official told a media technical briefing on the proposed legislation.

François-Philippe Champagne, the Minister of Innovation, Science and Industry, together with David Lametti, the Minister of Justice and Attorney General of Canada, introduced the Digital Charter Implementation Act, 2022. The ministers said Bill C-27 will significantly strengthen Canada’s private sector privacy law, create new rules for the responsible development and use of artificial intelligence (AI), and continue to put in place Canada’s Digital Charter.

The Digital Charter Implementation Act includes three proposed acts: the Consumer Privacy Protection Act, the Personal Information and Data Protection Tribunal Act, and the Artificial Intelligence and Data Act (AIDA)- all of which have implications for Canadian businesses.

Bill C-27 follows an attempt by the Liberals to introduce Bill C-11 in 2020. The latter was the federal government’s attempt to reform privacy laws in Canada, but it failed to gain passage in Parliament after the then-federal privacy commissioner criticized the bill.

The proposed Artificial Intelligence and Data Act is meant to protect Canadians by ensuring high-impact AI systems are developed and deployed in a way that identifies, assesses and mitigates the risks of harm and bias.

For businesses developing or implementing AI this means that the act will outline criminal prohibitions and penalties regarding the use of data obtained unlawfully for AI development or where the reckless deployment of AI poses serious harm and where there is fraudulent intent to cause substantial economic loss through its deployment.


An AI and data commissioner will support the minister of innovation, science, and industry in ensuring companies comply with the act. The commissioner will be responsible for monitoring company compliance, ordering third-party audits, and sharing information with other regulators and enforcers as appropriate.

The commissioner would also be expected to outline clear criminal prohibitions and penalties regarding the use of data obtained unlawfully for AI development or where the reckless deployment of AI poses serious harm and where there is fraudulent intent to cause substantial economic loss through its deployment.

Canada already collaborates on AI standards to some extent with a number of countries. Canada, France, and 13 other countries launched an international AI partnership to guide policy development and “responsible adoption” in 2020.

The federal government also has the Pan-Canadian Artificial Intelligence Strategy for which it committed an additional $443.8 million over 10 years in Budget 2021. Ahead of the 2022 budget, Trudeau [Canadian Prime Minister Justin Trudeau] had laid out an extensive list of priorities for the innovation sector, including tasking Champagne with launching or expanding national strategy on AI, among other things.

Within the AI community, companies and groups have been looking at AI ethics for some time. Scotiabank donated $750,000 in funding to the University of Ottawa in 2020 to launch a new initiative to identify solutions to issues related to ethical AI and technology development. And Richard Zemel, co-founder of the Vector Institute [formed as part of the Pan-Canadian Artificial Intelligence Strategy], joined Integrate.AI as an advisor in 2018 to help the startup explore privacy and fairness in AI.

When it comes to the Consumer Privacy Protection Act, the Liberals said the proposed act responds to feedback received on the proposed legislation, and is meant to ensure that the privacy of Canadians will be protected, and that businesses can benefit from clear rules as technology continues to evolve.

“A reformed privacy law will establish special status for the information of minors so that they receive heightened protection under the new law,” a federal government spokesperson told the technical briefing.


The act is meant to provide greater controls over Canadians’ personal information, including how it is handled by organizations as well as giving Canadians the freedom to move their information from one organization to another in a secure manner.

The act puts the onus on organizations to develop and maintain a privacy management program that includes the policies, practices and procedures put in place to fulfill obligations under the act. That includes the protection of personal information, how requests for information and complaints are received and dealt with, and the development of materials to explain an organization’s policies and procedures.

The bill also ensures that Canadians can request that their information be deleted from organizations.

The bill provides the privacy commissioner of Canada with broad powers, including the ability to order a company to stop collecting data or using personal information. The commissioner will be able to levy significant fines for non-compliant organizations—with fines of up to five percent of global revenue or $25 million, whichever is greater, for the most serious offences.

The proposed Personal Information and Data Protection Tribunal Act will create a new tribunal to enforce the Consumer Privacy Protection Act.

Although the Liberal government said it engaged with stakeholders for Bill C-27, the Council of Canadian Innovators (CCI) expressed reservations about the process. Nick Schiavo, CCI’s director of federal affairs, said it had concerns over the last version of privacy legislation, and had hoped to present those concerns when the bill was studied at committee, but the previous bill died before that could happen.

Now the lawyers. Simon Hodgett, Kuljit Bhogal, and Sam Ip have written a June 27, 2022 overview, which highlights the key features from the perspective of Osler, a leading business law firm practising internationally from offices across Canada and in New York.

Maya Medeiros and Jesse Beatson authored a June 23, 2022 article for Norton Rose Fulbright, a global law firm, which notes a few ‘weak’ spots in the proposed legislation,

… While the AIDA is directed to “high-impact” systems and prohibits “material harm,” these and other key terms are not yet defined. Further, the quantum of administrative penalties will be fixed only upon the issuance of regulations. 

Moreover, the AIDA sets out publication requirements but it is unclear if there will be a public register of high-impact AI systems and what level of technical detail about the AI systems will be available to the public. More clarity should come through Bill C-27’s second and third readings in the House of Commons, and subsequent regulations if the bill passes.

The AIDA may have extraterritorial application if components of global AI systems are used, developed, designed or managed in Canada. The European Union recently introduced its Artificial Intelligence Act, which also has some extraterritorial application. Other countries will likely follow. Multi-national companies should develop a coordinated global compliance program.

I have two podcasts from Michael Geist, a lawyer and Canada Research Chair in Internet and E-Commerce Law at the University of Ottawa.

  • June 26, 2022: The Law Bytes Podcast, Episode 132: Ryan Black on the Government’s Latest Attempt at Privacy Law Reform “The privacy reform bill that is really three bills in one: a reform of PIPEDA, a bill to create a new privacy tribunal, and an artificial intelligence regulation bill. What’s in the bill from a privacy perspective and what’s changed? Is this bill any likelier to become law than an earlier bill that failed to even advance to committee hearings? To help sort through the privacy aspects of Bill C-27, Ryan Black, a Vancouver-based partner with the law firm DLA Piper (Canada) …” (about 45 mins.)
  • August 15, 2022: The Law Bytes Podcast, Episode 139: Florian Martin-Bariteau on the Artificial Intelligence and Data Act “Critics argue that regulations are long overdue, but have expressed concern about how much of the substance is left for regulations that are still to be developed. Florian Martin-Bariteau is a friend and colleague at the University of Ottawa, where he holds the University Research Chair in Technology and Society and serves as director of the Centre for Law, Technology and Society. He is currently a fellow at the Harvard’s Berkman Klein Center for Internet and Society …” (about 38 mins.)