Published in the Montreal Gazette on December 29, 2017
By 2017-19 Fellows Bojan Francuz, Kara Sheppard-Jones and Nicole Leaver
Barely a week goes by in Montreal without a conference, event, lab opening or cataclysmic pronouncement about the promises and perils of artificial intelligence (AI). AI is generally understood as the theory and development of machines that can learn to mimic human behaviour, and can learn from themselves to solve problems. In practice, this means AI systems could help doctors diagnose diseases, support judges in the justice system and help choose the perfect candidate for a job. These opportunities are exciting.
Yet, in the development of AI, a number of ethical questions must be considered. One key concern, in particular, is often overlooked: bias in data.
Humans are full of conscious and unconscious biases. For example, a 2012 Commission des droits de la personne et de la jeunesse study in Quebec showed that in considering equally qualified and skilled candidates, those with last names like Ben Saïd were 35 per cent less likely to be called back for an interview than those with last names like Bélanger.
Our machines are learning from this data. They are being taught through AI systems that in fact “Bélangers” are more qualified than “Ben Saïds.” So, as we use AI to predict recidivism in the criminal justice system, to determine loan eligibility or for job application screening, we are further embedding systemic discrimination in our institutions. This is unfair and unethical. It is also a great economic loss.
Select individuals and emerging organizations are seeking to better understand and ultimately counter the phenomenon of “algorithmic bias.” AI practitioners are also engaging in these discussions, and Montreal experts are leading the way. In early November, the Forum for Responsible AI took place in the city. The event culminated in the launch of a crowd-sourced Montreal Declaration for a Responsible Development of AI, outlining seven principles guiding its creation (wellness, autonomy, justice, privacy, knowledge, democracy and accountability), and detailed a consultation process that will unfold in the coming months.
These conversations are key, yet we must also seek change beyond the creation of a declaration. As a group of young leaders from Canada and across the world partaking in the Jeanne Sauvé Public Leadership Program in Montreal, we are compelled to contribute to these discussions. Together in our program, we are analyzing the root causes of exclusion and finding ways to foster inclusion in culturally diverse societies. Through our work, we have identified two priorities that should be tackled to counter algorithmic bias.
First, to create unbiased systems, representation is imperative. As Fei-Fei Li, the Chief Scientist of AI at Google Cloud says, “If we don’t get women and people of colour at the table — real technologists doing the real work — we will bias systems.” Representation can help identify bias at the source and rectify biased systems before they are widely deployed — before it’s too late. By working against “algorithmic bias,” our hope is that AI can help us mitigate human fallibility and bolster opportunities for greater inclusion.
Second, we must turn our gaze inward to identify implicit and explicit bias in ourselves. Put simply: algorithms mirror the status quo. Therefore, our efforts to code a world without bias will only be as strong as our capacity to identify and uproot societal norms that perpetuate inequality. This will take an honest reckoning with how conscious and unconscious bias manifests individually and systemically.
Building an inclusive world is a radical idea. But so are self-driving cars and AI legal defence. Disruptive technologies reflect this generation’s ambition and desire to challenge the status quo. Yet, innovation left unchecked will only serve to exacerbate inequities by replicating biases. It will take a variety of minds to build an equitable and inclusive society, and in a future so ripe with possibilities, it is a coalition we cannot wait to build.