Published: January 19, 2019
We’ve seen far too many examples of artificial intelligence (AI) systems making harmful decisions or recommendations based on race, gender, or other protected classes of data (e.g., interest rates, predictive policing, lending apps, ride hailing services, AI assistants). Increasingly, we are seeing governments in the US, Denmark, China and elsewhere use AI to make decisions about criminal sentencing, who gets access to medical benefits, which children are most at risk of abuse, and who is eligible for schools, jobs, and government contracts slowly moving towards "algocracy" (rule by algorithms). The causes of bias and harm are complex and multi-factor, but there is a large group of women fighting to ensure that AI is created and implemented to help society rather than harm it.
On December 19th, 2018, 25 of those women from tech companies (e.g., Salesforce, Workday, Intel, IBM, Google, Amazon, Socos Labs), non-profits (e.g., Markkula Center for Applied Ethics, Omidyar Network, AI4All, Stanford’s Global Digital Policy Incubator, BSR), and analysts (Altimeter) came together for a day to share their experiences and insights, as well as to brainstorm solutions to big challenging we are facing.
“Every social justice movement that I know of have come out of people sitting in small groups, telling their life stories, and discovering other people have shared similar experiences.” - Gloria Steinem
The day began with a series of lightning talks to share with the group practical experiences that others can leverage in their own work. I gave a presentation sharing my lessons learned standing up the new position, Architect of Ethical AI Practice at Salesforce.
Dr. Ming spoke on many of the themes highlighted in her recent interview in The Guardian. She shared how she and others work on developing AI for good (e.g., treating diabetes, predicting bipolar depression) but that this always comes with frightening and complex ethical questions. like. In her words, “Technology is only a tool. It is an amazing tool, and one that has had, on balance, a profoundly positive impact on the world. But it can only ever reflect our values back at us. ...seemingly innocent technologies can have surprisingly negative effects, such as inequality, capture effects, and instability in social networks. In the end, technology should never simply make us feel good or ease us through our day; it must always challenge us. When we turn technology off we should be better people than when we turned it on.”
“AI for Good is easy; it’s AI-That’s-Not-Bad that’s hard.” - Vivienne Ming, Socos Labs
We got a personalized version of Tess’s talk at the Unintended Consequences of Technology event. She shared some disappointing statistics on the diversity crisis multiple sources including Element AI (2018), NSF Science & Engineering indicators (2018), Kapor, ASU, Pivotal Ventures, and AI Index.
AI4All wants to change this by expanding the diversity pipeline, increasing awareness of and access to AI education, and conducting research in AI for Good applications.
“There is a diversity crisis and it is urgent.” - Tess Posner, AI4All
I saw a lightning talk that Irina gave at the Partnership on AI (PAI) All Partners Meeting last month and loved it but it went by too quickly and we didn’t have the opportunity to ask questions but that wasn’t the case at the Summit! She talked about the need for “AI-Free Zones.” “The public conversation is full of hype and misinformation about what algorithms can do 'better' than humans can. Are there problems or areas of human life in which automated decision-making will not help, and might, in fact, cause more harm? If so, what might those be, and how should we improve the conversation?" Potential areas: parenting, relationships, religion/faith.
"Algorithms can't be used to decide societal norms.” - Irina Raicu, Markkula Center for Applied Ethics
Susan, who recently published an AI Maturity Playbook, shared with us the Five AI Trends to Watch for in 2019:
She also made predictions about three possible outcomes for 2019:
“We're not done yet--not by a long shot. Publishing ethical principles and assembling ethics teams is a good first step. It looks super on a press release. But this is where the real work begins.” - Susan Etlinger, Altimeter Group
Vocabulary is always a topic of discussion in the AI Ethics world. Words like “fair,” “bias,” “transparent,” and “ethical” can mean different things to different people. Priya highlighted the difference between two concepts that are important to distinguish when discussing AI fairness or bias checking tools:
IBM’s has an impressive set of open source AI Fairness resources (AI Fairness 360 Open Source Toolkit) including tools to check for bias and to overcome it. However, tools alone are not enough. “AI is not only a technical problem.” We must change the incentive structure within our organization. Most for-profit companies incentive employees based on revenue, clicks, user adoption, etc., which can be counter to making difficult but ethical decisions. As a group we discussed what those might look like (e.g., reward when a project is canceled due to concerns about societal impact, reward when a sales rep. identifies a potential customer that would violate the company’s values).
“We need more human input in the system.” - Priya Vijayarajendran, IBM
Chloe presented on behalf of her two colleagues, Heather and Iman, who were sadly out sick. They shared some of the best practices for integrating AI Ethics into the product life cycle as well as some of the tools and capabilities to ensure AI fairness by detecting and fixing bias.
Intel offers many courses in ethics for AI for data scientists and non-data scientists. A discussion among the group followed about how to make training courses engaging and accessible by (e.g., existing knowledge, learning style, time, location). How do you know if someone retained the training and is applying it (i.e., what’s the impact of the course)?
“I foresee workforce development, recruiting, diversity and inclusion teams, data scientists and product teams working more closely together to identify areas where bias enters data systems and improve product quality.” - Chloe Autio, Intel
Several ideas were brainstormed at the end of the day about potential ways one might drive change in their organization. Not all of them make sense for all organizations or should be attempted all at once but this is a great list to get ideas from!
“It can feel isolating when I am the only one working on these issue in my company but I have a community now that I can turn to. I am not alone. We can do this together!” - Summit Member
I couldn’t be happier with how the first Women In AI Ethics Summit went and I look forward to many more to come! If you want to participate in future AI Ethics Summits, please let me know!
“My brain is tired but my heart is full!” - Summit Member
A special thank you to Mia Dand at Lighthouse for raising everyone’s awareness of the brilliant women in AI Ethics and to Danielle Cass, Director of Ethical AI at Workday for suggesting this event and recruiting many of the brilliant women in the room! And thank you to all of the women who joined us to share their ideas, experience, energy, and light!
Thank you to Tiffany Testo for all of your feedback and support on this article!