Human-Centered AI

By Hollen Barmer, R. Dzombak, M. Gaston, Vijaykumar Palat, F. Redner, Carol J. Smith, Tanisha Smith. IEEE pervasive computing, 2021

502 citations recorded by the source corpus.

We identify three specific areas of focus to advance human-centered AI:• Designers and systems must understand the context of use and sense changes over time: Successful AI Engineering depends on the team’s ability to identify and articulate the desired system outcome and understand human and contextual factors affecting the outcome. The system itself must be able to learn when shifts in context have occurred. What are the best ways to maintain clarity around operational intent and mechanisms for adapting and evolving systems based on dynamic contexts and user needs?• Development of tools, processes, and practices to scope and facilitate human-machine teaming: Implementation of AI systems entails high levels of interdependence between human and machine. Adoption of AI systems requires the primary users to interact with and understand systems, gaining appropriate levels of trust. Every AI system needs to be designed to recognize boundaries and unfamiliar scenarios, and to provide transparency regarding its limitations.• Methods, mechanisms, and mindsets to engage in critical oversight: AI systems learn through data and observations, rather than being explicitly programmed for a deterministic outcome. Critical and reflective oversight by organizations, teams, and individuals that create and use AI systems is needed to uphold ethical principles and proactively consider the risks of bias, misuse, abuse, and unintended consequences through design, development, and ongoing deployment.For each area, we identify ongoing work as well and challenges and opportunities in developing and deploying AI systems with confidence.

Subfields: Management Information Systems, Safety Research

View Paper: https://www.semanticscholar.org/paper/264b9b136889da3b4d7e50ef58c77678b35dc3e0

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In Safety Research

1. Percy Liang
2. Michael S. Bernstein
3. Patrick Hall
4. Mark Coeckelbergh

Citations in the corpus

• Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, background): “” Human-centered AI (HCAI) is an emerging area of scholarship that reconceptualizes HCD in the context of AI, providing human-centered AI design metaphors and suggested governance structures to develop reliable, safe, and trustworthy AI systems [258].”
• Six Human-Centered Artificial Intelligence Grand Challenges (2023, unknown): “(Shneiderman, 2022), efforts must go beyond optimizing”
• Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation (2023, background): “Active learning allows for the integration of users’ feedback while models are learned, and enables interactivity with the user, one of the goals targeted by human-centered AI [73] and AI for social good [21].”
• Generative Agents: Interactive Simulacra of Human Behavior (2023, background): “As research progressed, it became evident that autonomous agents could ofer new metaphors for delegation and interaction [68], but the boundaries of delegation between humans and agents have remained the subject of ongoing debate and refnement [47, 89, 90].”
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice (2023, background): “…that participation can enable AI systems to better reflect the values, preferences, and needs of users and other impacted stakeholders, or more broadly, that participation will empower stakeholders in shaping the design of AI systems [3, 7, 19, 26, 33, 51, 61, 118, 137, 143, 172, 189, 198–200].”