Interactions - January/February 2019

groups in industry and academia pushing this conversation, including the People + AI Research team (PAIR) at Google (you can see our thinking on a human-centered approach to AI at https://design.google/library/ai/), the AI Now Institute, and the Algorithmic Fairness and Opacity Working Group.

Our call to action is to employ a global lens to our core AI assumptions, whether it is the training data, model performance, explainability of systems, or deciding which human needs to address with AI in the first place.

Only then can we truly make AI work for humanity.

Endnotes

1. CIA World Factbook, 2015; https://www.

cia.gov/library/publications/the-world-factbook/fields/ 2103.html

2. Thies, I.M. User interface design for low-
literate and novice users: Past, present
and future. Foundations and Trends®
in Human–Computer Interaction 8, 1

(2015), 1–72.

3. Sambasivan, N., Checkley, G., Batool, A., Gaytán-Lugo, L.S., Matthews, T., Consolvo, S., and Churchill, E. “Privacy is not for me, it’s for those rich women”: Performative privacy practices on mobile phones by women in South Asia. Proc. of SOUPS 2018. USENIX Association, 2018.

4. ITU Facts and Figures, 2017.; https://www.

itu.int/en/ITU-D/Statistics/Documents/ facts/ICTFactsFigures2017.pdf

5. UNICEF State of the World’s Children,

2017; https://www.unicef.org/publications/ files/SOWC_2017_ENG_WEB.pdf.

6. Ayyub, R. In India, journalists face slut-shaming and rape threats. New York Times. May 22, 2018; https://www. nytimes.com/2018/05/22/opinion/india- journalists-slut-shaming-rape.html

Nithya Sambasivan is a UX researcher in Google AI. She co-leads research on building human-centered AI in emerging markets with Jess Holbrook. She has a Ph.D. from UC Irvine and an M.S. in HCI from Georgia Tech. Her research has won top awards at HCI and IC TD conferences. → nithyasamba@google.com

Jess Holbrook is a UX manager and UX researcher in Google AI. He and his team take a human-centered and technology-inspired approach to building AI-powered products like Google Clips, Lens, and AI Y. He co-leads the People + AI Research (PAIR) group, to provide accessible AI to help people solve meaningful problems themselves. He has a Ph.D. in psychology from the University of Oregon. → jessh@google.com

CHECK THE POTENTIAL SOCIETAL ABUSE OF AI

As AI becomes more sophisticated in its ability to not only detect and recognize but also manipulate entities, it increases the risk of causing serious damage to various underserved communities. An important research area lies in proactively understanding the potential algorithmic manipulation of any personally identifiable or attributable content to cause harm to individuals. Deepfakes, fake images, and videos powered by deep learning present new challenges with manufacturing fake news, malicious content, and pornographic content.

Such synthesis techniques can cause serious harm to non-privileged groups through wider circulation on social networks, with broad social repurcussions. Take the case of Indian journalist Rana Ayyub, who discusses how she has been constantly harassed through deepfake pornographic videos made of her, causing massive reputation damage [ 6]. Such image-manipulation incidents travel virally and further impact online expression; in our research on gender equity, we found that 61 percent of women across seven countries, including Brazil, India, and Indonesia, proactively uploaded profile photos using non-face images like flowers, animals, landscapes, and group photos to avoid personal-image manipulation, based on incidents they had heard about in the news.

Safety issues are complex and require the joint involvement of technology policy, law and order, and institutional change for any lasting change. Safeguards against bad actors and anti-abuse management are essential in the formative design principles of systems. Technology moderation and takedown policies should grow to encompass various cultural contexts. We should practice inclusive and participatory design and always consider all the stakeholders of the system; even if we leave out

5 percent of stakeholders and the technology works well for 95 percent of cases, there is the potential for unintended consequences.

UNDERSTAND AI POLICY IMPLICATIONS GLOBALLY

A growing number of AI researchers are building laudable applications for social good domains in healthcare, agriculture, social justice, and more. At the same time, we should not lose sight of how current AI trends and policies on automation and digitization affect societies all over the world. Difficult, polarizing questions are being raised about the impact of automation on jobs, skills, and wages. Most Global South economies are heavy on the informal and outsourcing sectors, such as call centers, data entry, and low-level factory jobs. Entire industries are vulnerable to job displacement.

In our ongoing research on the future of work in vocational sectors in India, we find that most technicians have little to no awareness of the future-of-work discourse or development.

Skill reinvention of economically disadvantaged workers is key to resilience and job readiness in the future. Policy interventions for a jobless future like universal basic income are being proposed in the Global North, but such proposals need to take into consideration the realities of low incomes, corruption, and very large populations in other regional contexts.

Governments in the Global South are increasingly pushing for the digitization of nation states to eliminate middlemen, for social welfare decisions, and to increase resource-allocation efficiency.

Algorithms and data are now being used for human welfare in the Global South, for example, vaccine deployments and food rations. Citizenry are increasingly defined by digitization, such as the Aadhar program. In a context where large groups of people are already below the poverty line and have fragile access to social welfare, errors and biases in automated decision making can be serious (e.g., misallocating food rations).