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UTSG: State of AI Ethics

NOTE: The feedback from this session will be integrated into the research project that we have going on at MAIEI this summer where we are joined by interns from across the world working on this subject! For more information, check out: https://montrealethics.ai/meet-the-16-inaugural-maiei-summer-research-interns/

Centre for Ethics – University of Toronto is hosting the Montreal AI Ethics Institute and the local AI ethics community at their offices to discuss the very important subject of State of AI Ethics – there has been a surge in the interest on the societal impacts of AI, especially with a whole host of declarations and sets of guidelines that have been published trying to capture these impacts from different angles. Yet, there are quite a few aspects that are missing in this conversation, especially when it comes to how these efforts are funded, what is the underlying diversity in the teams that put together these reports/research and most importantly are we missing key, unrepresented voices that need to be a part of the conversation but those that don’t necessarily have access to media sources to emphasize their work and opinions.

In this session we’ll be looking to gain a holistic understanding by leveraging insights from a diversity of backgrounds and fields, both from a social science and technical perspective. We’ll be building on the work from the Research Internship Program project at MAIEI (material for that will be sent out closer to the session, please make sure to keep an eye out for the email around August 13/14).

Guiding questions for the session:

1) What are the unheard voices in the current discourse of AI ethics and how do we bring them into the fold of AI ethics enabling them to make meaningful technical and policy contributions? There are a set of AI ethics “elite” and influencers that are driving the conversation, agenda and research directions via their audiences on social media and prior connections from their work which are marginalizing the voices of the people who are on the ground facing the effects of automation.

2) Given the current deluge of declarations, guidelines, and other initiatives that are trying to map out the developments in the field of AI ethics, who are the most underserved audiences when it comes to implementing AI ethics in a practical manner? The ultimate goal of work being done in AI ethics needs to be beyond just academic and theoretical interest and instead help people implement these practices in their research and work so that we can mitigate harms emerging from irresponsible uses of AI systems.

Please visit the Eventbrite link to get more information on the readings and the schedule for the event!

https://www.eventbrite.ca/e/toronto-ai-ethics-state-of-ai-ethics-tickets-67720276169

U of T undergraduate co-wins prestigious research award at AIES Conference

Inioluwa Deborah Raji awarded best paper for detecting facial recognition bias in Amazon technology

U of T undergraduate co-wins prestigious research award at AIES Conference

Amazon’s facial recognition technology may be misidentifying dark-skinned women, according to U of T Engineering Science undergraduate Inioluwa Deborah Raji and Massachusetts Institute of Technology Media Lab research assistant Joy Buolamwini. This finding helped Raji and Buolamwini win “best student paper” at the Artificial Intelligence, Ethics, and Society (AIES) Conference in Honolulu, Hawaii. Held in January, the prestigious conference was sponsored by Google, Facebook, Amazon, and the like.

Their paper, which caught the Toronto Star’s attention, was a follow-up on an earlier audit by Buolamwini on technology from Microsoft, IBM, and Face++, a facial recognition startup based in China.

Origins of the research

Buolamwini’s earlier study, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” investigated the accuracy of artificial intelligence (AI) systems used by the three technology firms for facial recognition. Then-Microsoft Research computer scientist Timnit Gebru co-authored the paper.

Raji wrote that after reading about Buolamwini’s experiences “as a black woman being regularly misgendered by these models,” she wondered if her personal experience would hold true for a larger dataset containing samples of other dark-skinned women. This proved to be the case in the final analysis.

According to Raji, “Gender Shades” uncovered “serious performance disparities” in software systems used by the three firms. The results showed that the software misidentified darker-skinned women far more frequently than lighter-skinned men.

In an email to The Varsity, Raji wrote that since the release of Buolamwini and Gebru’s study, all three audited firms have updated their software to address these concerns.

For the paper submitted to the AIES Conference, Raji and Buolamwini tested the updated software to examine the extent of the change. They also audited Amazon and Karios, a small technology startup, to see how the companies’ adjusted performance “compared to the performance of companies not initially targeted by the initial study.”

At the time of Raji and Buolamwini’s follow-up study in July, Raji wrote that “the ACLU [American Civil Liberties Union] had recently reported that Amazon’s technology was being used by police departments in sensitive contexts.”

Amazon denied that bias was an issue, saying that it should not be a concern for their “partners, clients, or the public.”

Raji and Buolamwini’s study showed evidence to the contrary. “We found that they actually had quite a large performance disparity between darker females and lighter males, not working equally for all the different intersectional subgroups,” wrote Raji.

Amazon’s response to the study

In a statement sent by Amazon’s Press Center to The Varsity, a representative wrote that the results of Raji and Boulamwini’s study would not be applicable to technologies used by law enforcement.

Amazon wrote that the study’s results “are based on facial analysis and not facial recognition,” and clarified that “analysis can spot faces in videos or images and assign generic attributes such as wearing glasses,” while “recognition is a different technique by which an individual face is matched to faces in videos and images.”

“It’s not possible to draw a conclusion on the accuracy of facial recognition for any use case – including law enforcement – based on results obtained using facial analysis,” continued Amazon. “The results in the paper also do not use the latest version of Rekognition and do not represent how a customer would use the service today.”

In a self-study using an “up-to-date version of Amazon Rekognition with similar data downloaded from parliamentary websites and the Megaface dataset of 1M images,” explained Amazon, “we found exactly zero false positive matches with the recommended 99% confidence threshold.”

However, Amazon noted that it continues “to seek input and feedback to constantly improve this technology, and support the creation of third party evaluations, datasets, and benchmarks.” Furthermore, Amazon is “grateful to customers and academics who contribute to improving these technologies.”

The pair’s research could inform policy

Raji wrote that while it’s tempting for the media to focus on the flaw in Amazon’s software, she thinks that the major contribution of her paper is in helping to uncover how researchers can effectively conduct and present an audit of an algorithmic software system to prompt corporate action.

“Gender Shades introduced the idea of a model-level audit target, a user-presentative test set, a method for releasing results to companies called Coordinated Bias Disclosure,” wrote Raji.

In other words, Raji and Buolamwini’s research showed an effective way for companies and policymakers to investigate and communicate a problem in software systems and take action.

Most importantly, wrote Raji, the studies highlight the need for researchers to evaluate similar software models “with an intersectional breakdown of the population being served.”

Department of Engineering introduces artificial intelligence minor and certificate

The new program will be available to students in January

Department of Engineering introduces artificial intelligence minor and certificate

The Faculty of Applied Science & Engineering’s new Artificial Intelligence (AI) minor and certificate programs will be available for enrolment by students in the Core-8 and Engineering Science programs in January.

Students are required to fulfil three full course equivalents (FCE) to complete the minor, while students enrolled in the certificate program must complete 1.5 FCEs. Since a few of the courses required for the program fall out of the scope of students’ main discipline, some students may need to take extra courses to complete the requirements.

Students who complete the minor or certificate will receive a notation on their transcript.

Professor Jason Anderson from the Department of Electrical & Computer Engineering, a key figure behind the program, explains that all students will be required to take one foundational course, as well as courses in data structures and algorithms relevant to AI and machine learning.

Students enrolled in the certificate program can choose between traditional AI or machine learning for specialization. Students in the minor will learn about both and choose an additional area of interest to specialize in, such as computer vision or natural language processing.

Anderson explains that machine learning is one aspect of AI. In traditional AI, computers can make decisions on their own. In machine learning, computers use and learn from data to make decisions.

“The computer is actually trained to recognize images in different categories. In traditional AI, that’s more encoded in rules.”

“Students who take the certificate or minor will have hands on experience applying AI and machine learning techniques to real engineering problems,” says Anderson. In addition, students will be exposed to the ethical questions surrounding AI technology.

While there is currently no specific Professional Experience Year Co-op (PEY) opportunity for the AI minor and certificate, Anderson says that many students are already working with AI to some extent during their PEY.

Anderson also notes that AI ties in with other engineering disciplines in several ways. For instance, AI technologies can be used by civil engineers to understand traffic patterns or by chemical engineers in drug discovery.

In his own field, Anderson notes that AI technology is being used in computer-aided design tools that “create complicated digital circuits” in order “to produce higher quality designs, for example, that use less silicon area, that use less power, operate faster, to make predictions.”

“We want students who can research in this area but also have applied AI techniques,” says Anderson. Through this program, he hopes to foster engineering talent that will lead students to create startups, develop new AI technology, or further their education through graduate studies.

Rotman hosts AI industry leaders for machine learning conference

Alibaba president, Sanctuary AI founder among speakers discussing the future, impacts of technology

Rotman hosts AI industry leaders for machine learning conference

The Rotman School of Management’s Creative Destruction Lab hosted 24 of the world’s leading artificial intelligence (AI) researchers, business leaders, economists, and thinkers on October 23. The “4th Annual Rotman Conference on: Machine Learning and the Market for Intelligence” featured discussions of AI and the impact it will bring to the future of business, medicine, and numerous other industries.

Ajay Agrawal, the founder of the Creative Destruction Lab, and Shivon Zilis, the project director of Tesla and Neuralink, co-chaired the 11.5-hour event. Among the speakers were Alibaba — the world’s largest online retailer — President Michael Evans, Governor of the Bank of England Mark Carney, and U of T Emeritus distinguished professor Geoff Hinton. Despite their unique perspectives, one message was clear: machine intelligence will revolutionize how we think about solving problems.

The event began with talks from leaders in the international business sector on why industries worldwide are rapidly adopting machine intelligence into their business practices. Kevin Sneader, Global Managing Partner at McKinsey & Company, explained how monumental AI will be toward optimization and efficiency. Sneader said that he expects “mainstream absorption” of AI within the next decade. Evans showcased Alibaba’s automated distribution facilities powered by intelligent roving robots and its multitiered corporate strategy to adopt AI.

The speakers made it clear that businesses see the huge potential upsides associated with smart automation, but none discussed the issues that AI adoption may bring to the labour force or customer data responsibility.

Many industry pioneers dream of closing the gap between human and artificial intelligence, and they want you to know that the results don’t have to parallel dystopian sci-fi. Suzanne Gildert, CEO of Sanctuary AI, is building sentient, fully autonomous robots powered by the next generation of AI.

The artist-turned-technologist said that designing the first generation of synths with realistic human bodies will allow them to interface with our human world. Debates around the treatment, regulation, and integration of robots into human society are still very unresolved, but Gildert hopes that AI will push humankind to new heights. Citing the possibilities to create hyper-empathetic, creative, and intelligent minds, Gildert emphasized her optimism for the future of AI.

She ended her talk with a fascinating, albeit slightly terrifying, demo of a robotic clone of herself, complete with a matching silicon body and voice capabilities.

Perhaps one of the more sobering talks of the day was given by theoretical physicist and former president of the Santa Fe Institute Geoffrey West, who discussed the “socioeconomic entropy” that comes with chasing innovation. Despite the optimism of other speakers and the crowd in light of continued innovation and growth, West cast doubt over humanity’s ability to support sustained accelerated innovation.

Based on his research into the scale of companies and human networks, he suggested an underlying futility to the aspirations of the field. This alternate perspective brought a human context back to the event; if we don’t understand how we grow, we are doomed to collapse under our own weight.

PHOTO BY NIKHI BHAMBRA/THE VARSITY

The lower floors of the event hosted Toronto AI companies, who demonstrated their latest and greatest tech. Dozens of startups and corporations presented their efforts to integrate AI into solutions for specific industry problems, highlighting the extent of AI adoption.

In conversation with Shawn Malhotra

Toronto’s booming technology sector is promising for students and graduates

In conversation with Shawn Malhotra

Toronto’s technology sector is one of the most rapidly growing industries in North America, with a total of 212,000 employees in the field. This growth can be attributed to Canada’s overall economic growth and Toronto’s strong telecommunications presence, which includes the headquarters of Alphabet and Rogers Communications, to name a few.

Toronto also boasts a large and growing tech startup ecosystem, housing upwards of 2,500 active startups and 18 university-based incubators.

In 2016, Thomson Reuters opened the Toronto Technology Centre to leverage Canada’s highly-skilled workforce to provide customers with technology solutions using AI, cloud computing, blockchain, and more. The Toronto Technology Centre expects to create about 1,500 new jobs in Canada over time. 

Recently, The Varsity recently had the opportunity to speak with Shawn Malhotra, Vice-President of the Toronto Technology Centre and U of T alum, about the tech industry and what students can expect upon entering the field. Malhotra is a leader in deep data analysis, and previously served as Director of Software Development for the Programmable Solutions Group at Intel for 12 years.

The Varsity: You’ve been VP of the Thomson Reuters Toronto Technology Centre for over a year now. What has your experience been like so far?

Shawn Malhotra: It’s been great so far. I spent 12 years of my career at a past employer, so it was the first time in my career I had changed jobs. A big reason why I did that was I wanted to learn about new technology stacks and understand more about big data, cloud machinery, and these emerging technologies. Another big part was that I wanted to be building a new technology organization from scratch in Toronto. I’ve had a chance to do all those things and it’s been really rewarding.

TV: What initially attracted you to the field?

SM: When [Thomson Reuters] approached me, they started to describe some of the things I just mentioned, where I didn’t have a great concept of what kinds of technology problems Thomson Reuters was solving. If we apply those emerging technologies to the problems that matter to our customers, in a way you’re actually helping one of some of the most important decisions in the world get made more effectively. In the case of law, you literally have people’s freedom hanging in the balance. I just thought it would be really satisfying to learn about those things and apply them to some really important problems.

TV: How did your education shape your journey?

SM: I took advantage of a program that allowed me to do my Master of Engineering [at U of T] part-time while I was working. I worked not too far from the campus; it’s one of the advantages of being downtown in Toronto. I spent about four years doing a Master of Engineering [when] I did my coursework part-time while I was doing my studies.

I think it really helped me stay in touch with the research community and [make] sure that I was taking a wide perspective and a broad perspective to the challenges and opportunities I was seeing at work.

TV: What do you think makes the academia-industry partnership unique?

SM: Being immersed in that ecosystem, I think because we have such a breadth of problems to solve, there’s always some way for us to partner or work together. Being in those lab environments, and being in ecosystems like Communitech and institutes like the Vector Institute an academia and industry partnership around AI   has been fruitful for identifying those relationships and bringing thoughts, technologies, and business partnerships as well.

TV: Are you able to commercialize the technology that grows out of these partnerships?

SM: We call ourselves the ‘answers’ company, so we see our role as helping them effectively get to the right answer and employ technology to do that. Everything we do is finding ways to do that more effectively. Certainly, that means commercializing it and getting it that value to our customers. But absolutely, the labs are a good bridge into that academic part of the world and to figure out what we can take from there and commercialize.

Everyone in our technology industry needs to be plugged into research, thinking about emerging technologies, and thinking about how to commercialize it. And not just to make money, but to really serve our customers, which means that we’re solving these unique problems.

TV: What should recent graduates entering this field expect?

SM: I would say the one thing they should expect is the unexpected — it’s a very cliché thing to say, but technology doesn’t give one uniform experience to people. It’s been said a lot, and it’s true that basically every company in the world is becoming a technology company. [Graduates] should expect that they’re going to get choices and they’re going to be asked to learn new things.

To me, one of the most exciting parts of the field is that two years from now, we’re going to need very different skill sets than the ones we have today, because technology will have evolved. Fundamentally, what [graduates] should expect is to continually be learning new things, to be open to new experiences, to be open to different types of markets, businesses, and roles that they’re in.

TV: How can students prepare for the industry?

SM: I look back on my education, and I’m not differentiating equations at my desk, but what I was doing when I was studying calculus was learning how to master interesting, difficult subjects. I think as a student, if you see your role as learning how to learn, that’s great preparation for getting into the real world. It’s an approach of how to be practical with the knowledge you’re applying.

The thing I’d want [students] to prepare for is to turn that curiosity you have, and that ability to learn as a technologist into other parts of your company. The more you understand your customers and your business, the better you as a technologist are going to be to help identify ways to push them forward and help them.

This interview has been edited for length and clarity.

U of T student given Most Valuable Professional award by Microsoft CEO

Sabrina Smai reflects on her tech journey and how AI can spark global change

U of T student given Most Valuable Professional award by Microsoft CEO

Inspired by a technology course she enroled in by accident, fourth-year U of T student Sabrina Smai decided to switch gears from the world of medicine to the world of technology. Smai is a philanthropist who is deeply concerned about making a positive impact on the world. She is a strong proponent of using artificial intelligence (AI) as a tool to drive change.

This year, given her involvement and dedication to the field of AI, Smai was recognized by Microsoft and was given the Most Valuable Professional award by the CEO of the company, Sataya Nedella.

According to Microsoft, the Most Valuable Professional award is given to experts in the field of technology who “bring together diverse platforms, products and solutions, to solve real world problems.”

Smai said that when she received the award in the mail and saw Nedella’s signature on it she “started freaking out.” Smai added that “there are over 10 million people in the tech community around the world and only 4000 people get the award for the MVP.” According to Smai, she is one of the younger nominees to receive the MVP award and believes it was one her most memorable achievements.

Given the significance of the award and her expertise in AI, it is perhaps surprising that Smai went into U of T with the intention of studying life sciences in hopes of one day becoming a doctor: “growing up I always wanted to impact the world in some sense. I just didn’t know how! And the obvious choice was to be a doctor because you would see [the] direct impact you have on the world by helping patients.”

Smai, left, has been working in the tech field since she came to U of T. Photo courtesy of John W.

What caused this switch? Smai said that as a doctor she would “impact one person at a time.” However, in technology she “could impact the world in [a] very, very fast way.” Smai continued saying, “I wanted to be a part of a movement, as opposed to smaller change.”

And so, a drive towards greater impact on a global scale, led Smai to pursue her career in AI. Throughout this career, Smai has participated in several hackathons particularly related to artificial intelligence. It was through these hackathons Smai was first introduced to Microsoft Student Partners. “It’s a program run by Microsoft to help students gain more knowledge in cutting edge technology and really [get] in touch with the tech community. So, I got really involved [with] that.”

While working with technology, Smai has contributed a start-up called E-Terview. E-Terview is an application that uses facial recognition to help people gain confidence in their interviews: “essentially, you record yourself answering interview prompts and through the recording and [the application] would pick up at what part you were feeling nervous and at what part were you not as confident as you were in other areas of your interview,” explained Smai. In helping students succeed and overcome potentially difficult obstacles, Smai said her work on this application was one of her highlights working with AI.

Through projects like E-Terview, Smai could transform and create many other cutting-edge AI tools. These projects were no easy task as they required Smai to work long hours and have a strong dedication to her work. Passion and grit are essential in a field that requires these long hours, and Smai says in a quest to find a job it is beneficial to focus on one’s upheld values and beliefs. Smai is a strong believer in philanthropy and ultimately knew that her “end, end goal” was to help others through technology.