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Emerging Artificial Intelligence Issues: Discussion from the Third Cybersecurity and Technology Futures Event

March 15, 2019

Author:

Lovely-Frances Domingo

This event was the third in a series of events. The first focused on privacy and the second focused on systemic risk. The next one will focus on International Threats. It will be on April 3 at 3pm in the Peterson Room in the Allen Library on the UW campus. The full schedule can be found here.

The use of Artificial Intelligence (AI) in research and industry has been a hot topic lately—from facial recognition technologies and “deep fake” videos and photography, to social media trolls perpetuating fake news, to automated systems that decide job hires. For the third talk in a series of talks on Cybersecurity and Technology Futures, panelists from Microsoft and the University of Washington gathered on Wednesday, March 6 to discuss AI with iSchool Lecturer, Annie Searle, as moderator. Hannahneh Hajishrizi (UW), Sujatha Sagiraju (Microsoft), and Delight Roberts (Microsoft) discussed the emerging trends and risks associated with this fast-evolving field. Speakers spoke from the perspective of their deep expertise, rather than commenting as a representative of their places of employment.

Dr. Hannaneh Hajishirzi is an Assistant Professor at the Allen School of Computer Science and Engineering and a Research Assistant Professor for Electrical Engineering at the University of Washington. In her statements about emerging trends in the AI space, Dr. Hajishirzi explained that there has been great progress made in different areas and fields in AI, most notably in Natural Language Processing, computer vision research, cameras with face detection, and game play. She offered examples of this progress such as AI in home devices such as Amazon Echo and Google Home; AI in smartphones, such as Siri and Cortana; and the development of IBM’s Watson, which beat two Jeopardy champions in 2011. Dr. Hajishirzi is excited about moving beyond pattern matching, which is what her list of examples currently use, to the challenges inherent to implementing “common sense” knowledge and reasoning in AI systems. Despite progress, she considers data bias a big risk and a challenge in this space. To illustrate this point, she used an example from a sample data set that lists activities that people do such as “cooking” or “fixing.” These activities were then associated with male or female individuals. In the data, 60% of the individuals performing activities such as “cooking” were associated with women whereas men were associated “fixing.” Dr. Hajishirzi pointed out that because algorithms used in AI rely on line-by-line data, even data models—and by extension the algorithm— meant to fight bias can amplify bias rather than fight it because AI systems can’t do complex reasoning.

Sujatha Sagiraju is Group Program Manager for the Automated Machine Learning in the Azure AI group at Microsoft. Ms. Sagiraju shared her thoughts on emerging trends and risks in the AI space. She pointed out that one emerging trend is enterprise customers’ increasing desire to extract insightful data using available AI tools and infrastructure. This desire conflicts with their lack of resources, such as the personnel and the expertise/education to do so. Ms. Sagiraju noted this is an issue that is prevalent for many companies and institutions—the volume of data available is immense but there are simply not enough data scientists to develop solutions to gain valuable and actionable insights. She also cautioned that bias in the data set is still a risk in the AI space, and the lack of transparency in the data, process, tools, and algorithms used in the AI (also known as a “black box”) is an ongoing challenge. To address these challenges, she and her team have taken a range of approaches. For instance, one of their projects focuses on democratizing AI through automated machine learning along with “explainability” of the data processes and algorithms used. According to Ms. Sagiraju, this process helps enterprise customers increase their capacity to create their own AI solutions by enabling developers, data analysts and domain experts.

Delight Roberts is a Senior Compliance and Policy Strategist for the Artificial Intelligence and Research Group at Microsoft. She has a background in law, privacy, online safety, and external affairs. She focuses on policy, governance, and risk functions related to AI and research. When asked what emerging trends she sees in the AI space, she discussed significant improvements in healthcare using AI, such as improved cancer screening and the illustrated potential for solving critical healthcare concerns. Ms. Roberts talked about the role AI has played in Microsoft’s development of accessible technologies and in more efficient back-end data processing and analysis. She also mentioned that there is an intense focus on ethics, bias, and fairness. For example, as part of her work leading a Research Group, she works with teams of researchers, engineers, and business owners evaluate and undergo impact assessment before engaging in AI projects. When asked of risks associated with the AI space, she stated that there is currently no baseline public understanding of how data and AI is utilized by companies. Ms. Roberts believes that increased investment and understanding, both by governments and the public understanding of AI and how companies use data, would lead to more comprehensive and meaningful regulation to mitigate risks of data/AI misuse.

The talk was the third in a series of talks on Cybersecurity and Technology Futures. The next talk is on April 3, 2019 at 3pm in the University of Washington’s Allen Library’s Peterson Room. The speakers include: Jessica Beyer, Lecturer, UW Jackson School of International Studies; Jing de Jong-Chen, Senior Associate, Center for Strategic and International Studies; Katy Pearce, Associate Professor, UW Department of Communication. Jackson School Professor Sara Curran will moderate.

The speaker series is sponsored by the University of Washington’s Jackson School of International Studies, Information School, and Women’s Center with support from the Carnegie Corporation of New York.

This publication was made possible in part by a grant from Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author.