AI is changing nearly every sector today, from healthcare to employment to housing and beyond. In this episode, Commissioner Kosseim speaks with Professor Jake Okechukwu Effoduh, assistant professor at the Lincoln Alexander School of Law of Toronto Metropolitan University. They discuss the broader social impacts of AI systems on marginalized communities and why strong legal frameworks are essential for protecting fundamental rights and ensuring yesterday’s discrimination does not become tomorrow’s prediction. -- L’IA transforme presque tous les secteurs d’activité, des soins de santé à l’emploi en passant par le logement, et bien au-delà. Dans cet épisode, la commissaire Kosseim s’entretient avec Jake Okechukwu Effoduh, professeur adjoint à l’École de droit Lincoln Alexander de l’Université métropolitaine de Toronto. Ils abordent les répercussions sociales plus larges des systèmes d’IA sur les communautés marginalisées, ainsi que les raisons pour lesquelles des cadres juridiques solides sont essentiels pour protéger les droits fondamentaux et éviter que les discriminations d’hier ne deviennent les prédictions de demain.
Prof. Jake Okechukwu Effoduh Assistant Professor, Lincoln Alexander School of Law of Toronto Metropolitan University (TMU)
Jake Effoduh has gained significant expertise in international human rights advocacy at various ranks of domestic, regional, and international legal systems. He has also informed the regulatory frameworks and policy formulation on artificial intelligence (AI) both for supranational organizations and domestic institutions in several countries including the United States, Brazil, and Nigeria. Prior to joining TMU, Effoduh served as Chief Counsel of Africa – Canada AI and Data Innovation Consortium, mobilizing AI and big data techniques to build governance strategies. He is also the project coordinator of Canada’s Rights Role in Sub-Saharan Africa, a multi-year interdisciplinary SSHRC-funded partnership between Canada and several African countries. Effoduh has held multiple academic fellowships including at the Centre for Law, Technology, and Society at the University of Ottawa; the Harvard Library Innovation Lab of Harvard Law School; the Nelson Mandela School of Public Governance of the University of Cape Town; and the Center for Human Rights Science of Carnegie Mellon University.
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Patricia Kosseim:
Hello, I'm Patricia Kosseim, Ontario's Information and Privacy Commissioner, and you're listening to Info Matters, a podcast about people, privacy, and access to information. We dive into conversations with people from all walks of life about privacy and access to information issues that matter to them.
Welcome to Info Matters. AI is reshaping the ways we connect in nearly every sector, from business to healthcare, to everyday life. In today's rapidly changing digital landscape, it's important to consider how different populations experience AI differently and some of the risks that are inherent in the very design of these systems. In this episode, we'll explore different cultural perspectives on privacy, AI, and the deeper concerns emerging about equality, access, and transparency. We'll also delve deeper into why strong legal frameworks are essential to protecting our fundamental rights. Joining me today is Jake Effoduh, an assistant professor at the Lincoln Alexander School of Law at Toronto Metropolitan University or TMU.
His research has been at the intersections of international law, human rights, and AI. He's also informed regulatory frameworks and policy on AI for international organizations and domestic institutions in several countries, including the US, Brazil, and Nigeria. Prior to joining TMU, Jake served as chief counsel of the Africa-Canada AI & Data Innovation Consortium, seeking ways to mobilize AI and build appropriate governance frameworks. He's also the project coordinator of Canada's Rights Role in Sub-Saharan Africa or CARISSA, a multi-year interdisciplinary federally-funded partnership between Canada and several African countries. Jake, welcome to the show.
Jake Effoduh:
Thank you very much, commissioner. It is such an honour to be here with you. Such a big fan of all the amazing things you've done in sustaining leadership and making privacy a fundamental part of Canadian lives. So thank you so much for all your work in modernizing Ontario's privacy and access agenda. In this knowledge mobilization, like talking to people and expanding knowledge on this, absolutely phenomenal. So thanks for having me.
Patricia Kosseim:
Thank you for joining us. I'm so excited to get to know you better through this conversation and to get to know your work. And I must say, I find your bio so fascinating. Could you tell us more about your professional background across all these different continents and cultures and what inspired your research in this field?
Jake Effoduh:
Thank you very much, commissioner. I think I came to this work by way of two things I loved, human rights and public education, if I can call it that. In Nigeria before academia, I spent years in human rights advocacy on the radio speaking to millions of listeners about their rights. The right around issues of privacy or protection or discrimination was not very known where I was born and how I grew up. I could use tools like the local radio to sensitize and educate people about human rights issues. However, while I was very successful at that, I realized that the knowledge around human rights or letting the public become more aware of certain human rights violations did not automatically translate to justice. So I needed to get closer to power. And so I went to law school, studied law, and I started to practice. And I became a lawyer, I started to litigate.
I did criminal defense, I did public human rights advocacy, and then I moved into academia where I worked as a research fellow at the Nigerian Institute of Advanced Legal Studies. And then I did a lot of considerable work around human rights. And then I went for my master's in the UK at the University of Oxford, went back to Nigeria, I became a prosecutor. I worked on trying to help Nigeria return stolen assets by previous big political figures who had stashed monies abroad. I did that for two years. I've always been interested in advocacy and criminal justice. And so having done extensive work both in Nigeria, at the African Commission, at the economic community of West African States, I pushed a lot of resolutions with other groups of people at the UN. I worked with the UN independent expert on the protection against violence and discrimination around certain issues, including how to abolish torture in police stations and institutions across the African continent.
So all that was purely successful, though difficult and though varied in different ways. And I have always desired to move to Canada. That built up to me coming here to do a grad program at Osgoode Hall Law School. And then I did my PhD at York University as well. And interestingly, when I began working on artificial intelligence, the human rights component started getting more prominent in my work. So I saw how algorithms were shaping who gets a loan, who gets a job interview, who gets an apartment, a visa, or even a hospital bed. The exact same passion of why I became a lawyer just engulfed me again, and I've been writing, publishing, throwing more light on the human rights violations or the algorithmic discriminations from AI systems. But more importantly, what we can do as lawyers, what we need to do to ensure that AI systems do not obscure or exacerbate the sufferings of people who are already marginalized.
So that's the thorough line of everything I do, even at the Lincoln Alexander School of Law where I teach on research, AI governance, technology law. I try to show how justice must shape technology and not the other way around. How do we design a future where the technology does not influence justice, but justice influences the technology in ways that would be respectful and protective of people's fundamental human rights.
Patricia Kosseim:
Well, I was right. Your career path is indeed fascinating. I would've loved to accompany you through every step of that journey from knowledge mobilization of rights through radio to helping people exercise those rights to policy development, governing those rights. And the thorough line through all of that is justice. How excited and happy and lucky we are to have you here in Canada continuing the important work that you do here, right here at TMU. As I said in the intro, we are seeing AI touch nearly every aspect of our lives. It's affecting our jobs, education, housing, public safety, immigration, healthcare, and even access to basic public services. What are some of the important questions people should be asking to better understand the broader impact of AI systems on social access, justice, and equity, particularly for underrepresented or marginalized communities?
Jake Effoduh:
That's a great question, commissioner. And just to say that I'm also very hugely inspired by your work and your passion because you have put human rights at the center of public sector AI through your work, your collaborations, and you're helping us see that we need guardrails on police facial recognition, for example, some of the work that you've done. And I think your work shows us the kinds of questions we need to ask because we don't need to really understand what the technology is, where it's embedded or deployed. We just need to deploy critical thinking and ask the same questions we'll ask of any technology, any tool, any device. The first question is always like, is AI being used here? When you submit a job application somewhere or even you see a camera somewhere in a public space, school, even at the hospital, at the hiring portal or housing application, it's important that we ask, especially in Ontario, if AI has been used because employers should disclose AI use in screen for any publicly advertised job postings.
We should also need to ask what data fed into this system? Because an AI tool is only as wise as its diet, only as wise as its data. So the people whose data is being used to train the system or who the system prioritizes influences who is being positioned before that AI tool. And so whose experience is missing from that data? If a health algorithm, for example, was trained mostly on one population, we need to ask how does it perform on my population? We've seen this over and over again, the disproportionate impact on women or Black people or indigenous people. Not that the technology is bad in itself, it's just, do I fit in? Was it designed for me?
The same way you want to buy a dress, you want to be sure, is the size okay? Is it made for someone like me? Can it stretch? Can I use it for travel? We should also ask about getting an explanation. And this is something that you and your team has worked in helping providing tools. For example, how can I get an explanation that I can understand why is this decision coming out this way? One of the things I've noticed in my research is when people use AI to evaluate an applicant's employability, it somehow seeps into other organizations who use the same tool. So a person might apply to company A and they get a low ranking. If other companies use the same tool, if a person applies to those other companies, the tool might just take the evaluation from a previous company and give the same score.
And so can I challenge the result of an AI system? Where can I go to challenge that result? Who is a human being or institution that I can hold accountable? I remember when they started using AI in immigration. I traveled to Singapore. They though it was a good idea to have this tool, got my visa, but when I landed at the airport, they just couldn't read my passport. I was there stuck for hours until a human being showed up. And they were like, "Well, it never makes a mistake." But I was like, "Well, maybe you don't have travelers like me. So is there somebody else? Is there a human being or an institution I can go to if the system doesn't act accordingly?" And I think a lot of the work you've done around privacy is very key here. What happens to my data afterward?
Is it kept? Is it sold? Is it reused? Is it deleted? I think these are questions that we need to ask so that institutions can pay attention to what is important to us. As we say back home, a person who asks questions never loses the way. And because AI is so difficult to understand, even people who create it can't really explain it, there's all this black box dilemma. The questions we ask is more fundamental sometimes than even the regulations we propose.
Patricia Kosseim:
That's very interesting. I mean, questions are not only for the benefit of the individuals, as you say, but it's also a way of holding organizations accountable and having to address those questions. You mentioned training data and the importance of training data as the diet, if you like, of what we feed the algorithms. So I want to ask you, Jake, how can we ensure that the quality and the integrity of the data used to train these AI systems reflect the needs and rights of diverse groups and populations so that AI really does benefit all people?
Jake Effoduh:
Quality is multidimensional. When we talk about data quality, we're looking at the data provenance, the data accuracy, the relevance, the completeness, the representativeness, the timeliness. There's a whole lot around what makes data quality enough. So in my data science law class, we go into the intricacies of what is qualified as data, because sometimes we think it's one thing, but even data about data is also data, like the metadata, the connector, things that are even not directly agreeable to you also informs or influences decisions about you. So I always say think of data the way a chef would think about ingredients. Is it fresh? Is it what the label says it is? Does the packaging describe it in detail? Is it right for this dish? That would be the quality. The integrity of the data would ask things around, was it honestly sourced? Is it being used the way the provider expected it?
We have to look at the provenance. Where did the data come from? And how can we trace it? The accuracy, is it correct? Is it representative? Is it even relevant? Does it actually bear on the decision? Is it proxy sneaking in? Why do you need my postal code when we know that sometimes postal codes could stand in for race by these AI systems? When we talk about the quality, it's also about completeness. If you're going to make a decision about me, do you have a full picture? Does it cover my entire life experience to be able to adjudicate on whether I'm qualified for this job or not? When you're making a decision about the people, what about my population? What about my demography? Especially when it comes to health AI systems, what do you know about Black people and Black health in this area to be able to say this is solid or quality enough to make a decision?
There's contextual integrity. Information carries the norms of the context it comes from and when it moves to a new context, let's say from a clinic to a market or from school to police, there can be a violation right there. More data is always seen as better data, but more of the wrong data is just what will escalate bias, is what will just escalate prejudice. So if I speak about Black communities or Black people like myself, when we talk about integrity, we're seeing things around a misrepresentation of what it means to be Black or a miscalculation about Black people and Black populations. So it's a very important thing to think through, especially because quality data is not like, oh, here, I tick this box. It takes constant continuous questioning around provenance, around accuracy, around completeness, representativeness. And that's what I think data quality entails.
Patricia Kosseim:
Is part of a chef's ingredients. Very well said. Unfortunately, as you know, social inequities and discrimination already exist and need to be addressed as it is. What safeguards should be in place to ensure that AI doesn't further exacerbate these disparities? And can AI ever truly be fair in today's world?
Jake Effoduh:
In terms of safeguards against social inequality, we need to consider the necessity and the proportionality. An AI tool might do really well, but if it's going to disenfranchise or remove certain workers from their jobs, then we need to ask ourselves, is it proportional to getting us faster output? Do we want to sacrifice people's employment opportunities because we want to get faster output? Is it even necessary? Is this system actually needed at all? So those questions around necessity and proportionality is very important. I think you've highlighted some work around developing a human rights AI impact assessment. I think your office has championed privacy impact assessment. These are not options. When you are developing an AI tool, you have to look at the privacy impact assessments, the outputs of what you think this tool will do and take you through the entire life cycle from design to deployment.
In fact, when we last met at the Queen's University Workshop in February where you spoke, commissioner, you definitely talked about these no-go zones. You talked about using health data for insurance, underwriting, issues around discriminatory profiling, and that we need to ensure that these are workable across both federal and provincial division of powers. And I like that you make that clear because sometimes we can't really predict what an AI tool will do. If we don't make that assessment, we can't prepare for the pitfalls. We have to continuously monitor for disparate outcomes. And the truth is every AI system I have studied in the last seven, eight years of my research on this, there's always going to be disparate outcomes. That's why I like the proposal for a rights to appeal AI systems. It's very important because we need to enforce some level of protection and legal protection to ensure that if we really care about safeguards and we care about people trusting AI systems and we care about reducing social inequities, these are some of the things we have to think about.
Prohibition zones where we don't need to use AI at all, the procurement impact assessment rules for why, if, what AI tools we need to use. And of course, remedies we can actually enforce and follow through. So those are some of the safeguards I think we need to think about and deploy when it comes to protecting, especially marginalized groups in our communities. And then if AI can truly be fair, the direct answer is AI cannot be fairer than the data and the institutions and the power structure that it operates within. No matter how fantastic an AI tool is, no matter how well-developed it is, no matter its goals it wants to attain, it cannot do it outside the structures that it's embedded in, because AI learns from historical data and historical data is never a neutral record. It's just a ledger of society. So all the injustices that we see, the AI tool will have to work within that system.
And so if we have decades of hiring certain names or decades of overpolicing certain neighborhoods or decades of over-researching subjects, some subject areas are not the others, we're going to see a feedback loop. Yesterday's discrimination is going to become tomorrow's prediction. But I have hope from my research because AI can make decision patterns explicit. We can measure it more. I think right now we're able to expose discrimination in ways that were previously deniable. So if you have a human manager who is biased or not very good at making decisions, they leave no audit trail. But with a biased AI model, we can test it, we can audit it, and we can correct it at scale. So when companies are shy that their AI tools are biased or they feel embarrassed, and I'm like, it might be a reflection of your institution. Yes, that's quite all right.
But no AI tool is completely fair. No AI tool is completely free from error or discrimination. It's about what do we then do now that we see this bias, now that we see this error? How can we dismantle? How can we rejigger the algorithm to ensure that we use the information from the AI systems to rewrite the rules? So yes, AI can make things fairer, but it requires this dance of pulling the history and making the assessments and also following the audit trail to propose remediation.
Patricia Kosseim:
You mentioned that meeting where we met a few months back organized by Queen's. And I remember on the panel we were both on that you referred to different perceptions of privacy across various African cultures, and in particular, you mentioned that the meaning of privacy from a Kenyan perspective is very different say than our North American view of privacy and I was hoping that you can elaborate on that a little bit more.
Jake Effoduh:
Yes. Neither the Canadian or even African traditions are monolithic. It might be tempting to say, "Well, the West has an individualistic perception and Africa has a more communal," but I think there's richness on both sides. In Kenya, when I was working as a human rights advocate, going to the African Commission, there was a case brought up by the indigenous people, the Endorois people of Kenya, the Maasai people who live around the ancestral lands of Lake Bogoria in Kenya. And they were arguing before the African Commission the collective rights to land, the collective rights to culture, the collective right of meaningful consultation. And the African Commission had to decide, wait, so there is private privacy, individual privacy, but there is also communal privacy. And in that case, the Endorois people of Kenya were talking about data about a community that where one person's data has been infringed upon, it somehow affects the data sets of the entire community.
So that's this ideation of collective rights, that health patterns, issues around the climate and the neighborhoods we live in, those data sets should be collectively protected, not just about protecting the individual rights. We obsess so much about my privacy, my privacy, and then we lose that there are avenues to think about the collectivity, the connections, the benefit sharing. So that's what I raised on that panel, and I'm happy it resonated with you, and I'm happy that we're beginning to see that privacy moves beyond the individual. It also links the communities too. It links us together.
Patricia Kosseim:
Your description of this collective communal sense of privacy in Kenya is not unlike indigenous conceptions of privacy here in Canada. So I think there's a lot of synergies and a lot we can learn across cultures in helping us understand the broader implications of privacy beyond this individualistic conception. Black communities or indigenous peoples, racialized groups, persons with disabilities, and other marginalized communities are often discussed as passive subjects of AI regulation rather than active participants in helping shape it. So Jake, how can we better integrate and include diverse voices in policy conversations and raise the level of what you call race consciousness in AI governance?
Jake Effoduh:
Today, Black communities in Canada often experience a double distortion where data can confirm benefit. We as Black people are quite underrepresented in clinical data sets, in training diagnostics tools, in credit histories. There's not much representation. And I like that you talk about the need to move away from being just passive subjects. So that's why in my work I talk about Black data justice. And I think it's about Black people, not just as objective data systems. 'Cause I like it when people say, "Oh, Jake, by the way, did you know that we have a strong Black representation in our data? Did you know that one of the people who built this AI tool was a Black person as well?" I'm excited. I give the kudos. I'm like, "Thank you very much. It's good that we have more Black representation 'cause we know fully well that when algorithmic bias happens, it's more for marginalized and minority groups.
But we need to move also into how Black people can become authors and designers of the systems. In my Black Futures by Design Reports, which I have published at the Lincoln Alexander School of Law, instead of waiting for the Canadian government to propose a legislation on AI, and then Black people are going to be like, "Well, it doesn't cover this issue with us. It doesn't offer protection for marginalized or indigenous groups. Why don't we design or help design what that law should look like?" So I convened a conference with 70 Black experts across Canada, and we sat in a room for a whole day trying to say, "What exactly would you like the government to prioritize? What would you like our government to take note of?" Black people in Canada know that we already have histories that have disenfranchised us. How do we ensure that we don't have a repeat or exacerbated performance with AI systems becoming the order of the day?
It's about recognizing that AI is not neutral and it's not purely technical. It's a social legal construct that reflects the power structure that is built within the systemic marginalization of Black communities across criminal justice, employment, financial services. They will be reproduced through algorithmic systems. As Black communities, we're not asking for anything special. We're literally asking for everything you have been doing through your office is to say, "What about transparency? What about accountability? What about community oversight?" Because I, as a Black person, I'll tell you this, I have engaged with so many systems that people are like, "This is amazing. This is fantastic." But when you ask people of your kind, you realize that, well, it was fantastic for everyone, but not for us. The facial and recognition system helped everyone process through an airport really fast, but not for us. When we talk about what does Black justice look like concretely, I think about participation, that Black communities should be at the table when systems affecting them are designed.
I also think about assessment. What are the mandatory privacy impact assessments that we're taking into consideration before deployment in things like policing or housing or education? Because we know that we disproportionately don't have that leverage and history hasn't been as kind to us as it can be. And then three, I think about benefits. I believe AI can do so well for us, for Black communities, but it needs to be with intention. It needs to represent Black community data as valuable and not just as an inconvenience. It needs to appreciate and even provide remedy, so to speak. For me, I'm more comfortable if I walk into a hospital and the doctor has treated other Black people before. I'm like, "Ah, I feel a sense of this person knows what they're doing." So at the end of it, for me, it's just stewardship. How can Black people help improve technology? How can Black people be a part of the governance, be a part of the remediation?
How can we offer our own collective intelligence to design the future where an Ontario and in Canada can be and continue to be the great value that it has of multiculturalism and equity? There's a program in my language, and I'll say it's in Igbo, and then I'll translate it. It says, "If the left hand washes the right hand and the right hand washes the left hand, the conclusion of that is that both hands become clean." And so if indigenous communities, if persons living with disabilities, if our Black communities, queer communities, if our senior citizens, if we all are helping each other in the way that we can, yes, we might see friction here and there. Yes, we might not understand things here and there, but where the left hand is washing the right hand, the right hand is washing the left hand, there's an exchange, both hands will become clean.
So when you are pressing for guardrails on facial recognition and surveillance by police, when you are asking for meaningful transparency when it comes to AI-assisted hiring, when you are protecting children and youth online, I feel like that's the kind of hand-washing we need with government, with the private sector. And now with the public through this podcast, that's the kind of activity we need to get the hands clean.
Patricia Kosseim:
You've spent a lot of time thinking deeply and thoughtfully about what an AI governance regime should look like if you're at the table from the very beginning designing a system of AI governance. So I wanted to ask you, Jake, in what ways do you think Ontario's current AI governance framework can be strengthened to protect all Ontarians?
Jake Effoduh:
One of the biggest privileges in my life, if not the biggest, is that I get to live in a country like Canada and I get to be in a province like Ontario. We're very forward-thinking, we're very progressive. A lot of things that are impossible elsewhere are possible here. So for example, Bill 194, which creates the Enhancing Digital Security and Trust Act in 2024, it's something that I think we can build up on that. There is no perfect legislation, there's no perfect regulation. We have to keep thinking about how do we ensure equality goals? How do we ensure accountability? How do we ensure public participation? It's not perfect. We're not there yet. The same thing about Canada's AI strategy, which was released in June this year. It is ambitious. It is progressive, especially on investment on sovereign compute and adoption. And I like that there's been now lots of conversation around privacy modernization, but there's still a lot of work to be done.
There's still a lot of work to be done around where AI touches people the most, especially in hospitals, in schools, in police services, in social assistance, in landlord-tenant matters. These are largely provincial. And so Ottawa cannot regulate an Ontario hospital diagnostic algorithm the way that it can do for a bank. And this is why I'm quite passionate about the work we are doing here in Ontario to ensure that we're able to meet at the exact point of needs where people have their housing, their healthcare. And I like where we are. I like that the conversations are happening. I like that the hand washing is taking place. How do we tell stories that carries everyone along? How can this strategy help us realize equality outcomes and not more about dollars invested or products that are commercialized? How do we legislate the right to explanation, the right to contestation, the right to redress?
How do we help public procurement and registries to advance indigenous data governance on indigenous terms? How do we modernize privacy law, which you've been talking about ... I think these are the things we need to enable community participation. These are the questions I think about because we have to judge an AI strategy. And the way you judge a bridge, it's not by the ribbon cutting. It's about how many people can safely cross, how many people can pass through that bridge? I feel like we just need to keep asking more questions. We need more people listening to podcasts like this. We need more people reading those documents, which I know a lot of which have been published by your office.
My job is to take them to my classroom and expand it so that young lawyers are aware of what these bills are doing, the legislative testimonies that can help better the lives of people in the province. Because at the end of the day, no matter how fantastic we invest in AI, no matter how many ribbons we cut, how many strategies we print and produce, if people's lives are not getting better in the province, then we need to ask ourselves if we're really doing the work that is meant to be done.
Patricia Kosseim:
Jake, thank you so much for joining us today and for your thoughtful contributions on the topic of privacy, human rights, AI, and culture. I just want you to know I've learned so much from you and found this entire episode so interesting, fascinating, and thoughtful and intentional. And I want to thank you for all the work you're doing here in Canada, in your classroom, in your governance advocacy in order to advance the values that as a society we hold very dear. So thank you so much for joining me today.
Jake Effoduh:
Thank you so much, Commissioner Patricia. You are an erudite avant-gardist. To be able to do the work that you do and still make it a point of duty to mobilize knowledge about the work that you do is a doubling trend. It's not something we see quite often. I said it to you at Queen's when we met, but as Canada is waiting to go fast on AI, I think you are insisting that Ontario, we can go far, but we can also go together. And I like that you are carrying us along, you are passionate about your work, you're thinking about marginalized groups and marginalized communities. You are making sure no one's rights is left at the side of the road. And I respect that, but I also honor it and I really value it. And I want to say thank you so much for your work and for the passion that you have and the service to Ontarians and to Canadians at large. Thank you.
Patricia Kosseim:
Thank you, Jake. It's been an honor and privilege for me as well. This conversation reminds us that AI is moving at a pace that makes it challenging for policymakers and governments around the world to keep up. My office has urged the government to strengthen AI governance frameworks in ways that respect the fundamental rights of all Ontarians. Ontario's Enhancing Digital Security and Trust Act, or EDSTA, adopted in November 2024 is a small initial step in this direction. It sets out a framework that enables the government to adopt eventual regulations governing the use of AI technologies by public sector entities. To date, however, those regulations have yet to be made, and we're still waiting to see what those rules will be. In the meantime, our office has worked jointly with the Ontario Human Rights Commission to develop principles for the responsible use of AI. These principles are designed to help organizations develop, deploy, and use AI in ways that maintain public trust by respecting privacy and human rights.
For more information, please visit our website at ipc.on.ca or view the links in the show notes. I encourage all of you to chime into the conversation along with Jake and others to make your views and perspectives heard for a future of AI governance that ensures benefits for all Ontarians. Well, that's it for this episode, folks. I enjoyed it thoroughly and I hope you did too. Thanks for listening and we'll catch you next time.
I'm Patricia Kosseim, Ontario's Information and Privacy Commissioner, and this has been the Info Matters Podcast. If you enjoyed the show, leave us a rating or a review. If there's an access to information or privacy topic you'd like us to explore on a future episode, we'd love to hear from you. You can comment on our posts on Bluesky and LinkedIn or email your ideas to podcast@ipc.on.ca. Thanks for listening, and please join us again for more conversations about people, privacy, and access to information. If it matters to you, it matters to me.