Below is the full interview with AI Ethicist Merve Hicock for the 2022 Q1 EmTech Connect for the Women in Emerging Tech Interview Series. 

  • What role do you play in narrowing the skills gap in Data Science, AI or Data Ethics? I wear multiple hats in both data and AI ethics fields. With my own organization, I created one of the first global repositories of research focused on AI ethics – really to make it easier for researchers, journalists and those who are newly interested in the topic to find interesting and accessible works. The content is curated and regularly updated. These topics can be intimidating at times and some people think that because they do not have technical education they could not work on these issues or question their impact. I also try to make these concepts easy to understand to both individuals and organizations through my self-paced training courses online, or organizational consulting and training work. In addition, I am a lecturer at University of Michigan, teaching Data Science Ethics in our Masters’ of Applied Data Science program. Majority of our students are in professional work already so we discuss what concepts and issues are most relevant when we talk about data and AI and its impact in our lives, and how to apply these concepts to our work. And finally, I am also the Research Director at the Center for AI & Digital Policy. At the Center, we run semester-long AI policy clinics for graduate students and professionals across 39 countries, and teach about how to research, analyze and influence AI policy and regulations towards democratic values and human rights. 
  • What challenges have you faced as a woman in your field? Multiple challenges…not only as a woman but also as an immigrant woman of color. In this field of emerging technologies, when you question the discriminatory, exploitative or manipulative impacts of Big Data and AI systems on people, society and environment, there are some attempts to minimize the importance of your questions (especially if you are not coming from an computer/engineering background). You also have to work harder to make your voice heard, or get support for the questions which challenge certain corporate incentives or profit-driven decisions. These are crucial points to also determine the claims of a company presenting itself as responsible / ethical in its practices. How are their practices reflected in their hiring? What kind of workplace culture and environment is present? What incentives do the employees have to consider multiple angles? What mechanisms do they have to flag their concerns? 
  • Who was your greatest mentor or inspiration professionally? If I look back to the totality of my professional life, then I would thank one of my former COOs. He taught me a lot of skills about how to handle high-risk high-tension situations and how to keep the humour. He did not preach things, he was actually a role model in action – in pushing for inclusivity, standing up for what was right, and supporting the aspirations of those around him. If I look at the pioneering work in technology & society, then there are some pioneering women like Ruha Benjamin, Safiya Noble, Alondra Nelson, who inspire my work, give me the language to bring in critical inquiry and express the structural issues I see. I have huge respect for both their professional work and how they lift others around them. 
  • With such a demand for data science roles, what non-traditional paths have you found to be beneficial for employers seeking to create data teams? Good data science teams are multidisciplinary. It is not only about hiring those who can use data analytics tools or code, but who can help make sense of that data in the context you need to use it. For example if you have a data / AI product/service in healthcare, you need employees/stakeholders in your team who can advise on what certain medical data mean, why some data might be unexpected or possibly wrong, or why there might be rare occurrences you should be aware of to understand the limitations of your conclusions. You might need someone with a sociology background to advise you of certain societal or historical contexts which might be impacting your data’s quality and representativeness. It is crucial for employers to understand the importance of diversity of paths and backgrounds and how they can contribute to the quality of the products and services they offer. 
  • What characteristics does a person need to begin (or advance) their career in data/AI/ ethics? I get asked this question a lot and have actually written an article about it a while back and would like to share here “What does an AI Ethicist Do? A Guide for the Why, the What and the How?” We need diverse perspectives in the field so, first and foremost, we should not pigeonhole these careers into a certain professional or academic discipline, like law, philosophy, or computer science. We need people experienced in a variety of fields and previous roles, and ideally those that prepared the person to ask critical questions, respect diversity of opinions and interdisciplinary work, and consider the ethical, social, and political aspects of an AI technology. Being an active listener and not afraid to ask questions, while also building trust is important. Especially if working within an organizational environment, communication and trust are key elements. A single person in an organization cannot and should not try to change the direction alone. Ethical and responsible data and AI work requires the contribution and commitment of everyone in the organization – from Board and C-level down to new hires. It cuts across all departments. The ethics leader in this ecosystem should be visible, engaging and be empowered to drive change while building the buy-in from different stakeholders. In other words this person drives the strategy forward as the dedicated leader. However the ultimate goal should be to get to a mature state in the organization that responsible and ethical data & AI development is second nature to everyone and it is embedded in all the work.  
  • What additional advice would you offer to women looking to enter your field? I always say ‘find your tribe’ and build relationships and be authentic. This work can get stressful and political at times, so those networks can be your support mechanisms too if needed. It really helps, whether you are preparing yourself for such a career or you are already in one, to have the internal and external network where you can discuss ideas, learn new perspectives, and follow new developments. Do not be intimidated or be pessimistic about the amount of work required in this field or the negative news on AI / data bias or harms we see day in day out. It means that there is a lot to do and that every effort helps. Do not wait until you feel like you are the ‘perfect’ candidate for a role or that you match all the requirements 100%. If you believe in yourself and do not underestimate what you can bring or learn on the job, there will be others who will believe that too.  And if you have the means to support someone who might be facing similar challenges and lift their voices, do so. It takes a village.