Discrimination and Discernment in AI

Jun 3, 2024

Discrimination and Discernment in AI

Part 2: Human Centricity in AI

Overview

  • Increased use of AI in everyday life raises questions about AI-enabled decisions.
  • Focus on the importance of human-centric approaches to AI.

Issues with AI

  • AI can produce unexpected or biased conclusions due to human design and training datasets.
  • Example: 2019 research by the US National Institute of Standards and Technology showed racial bias in facial recognition AI.
  • Causes of bias: design flaws, biased training datasets.

Identifying Bias in AI

  • Purpose of AI differentiation: Discrimination vs. Discernment.
    • Discrimination: Personal basis or prejudice.
    • Discernment: Objective and without prejudice.
  • Example: 2015 US online retailer's hiring algorithm biased against women.

Minimizing Bias

  • Methods:
    • Human in the loop: Humans interact with AI during its operation.
    • Human over the loop: Humans oversee AI operations and can intervene.
  • Example: Company A categorizing customers by financial capability with human oversight to prevent bias.

Importance of Accountability

  • Critical for AI design and deployment to maintain confidence in AI solutions.
  • Approach: Ensure awareness of common AI lifecycle risks (design to post-deployment).

Best Practices for Accountability

  1. Design Thinking: Engage and empathize with stakeholders to understand concerns.
  2. Understand User Concerns and Intent: Ensure AI addresses user needs.
  3. Identify, Categorize, Quantify Risks: Facilitate risk management.
  4. Align AI Design with Departments: Check and balance for accountability.

Ethics and Transparency

  • Essential for demonstrating accountability to stakeholders.
  • Communication is key:
    1. Clear explanations of AI decisions.
    2. Easy feedback mechanisms.
    3. Human representative access for issues.
    4. Opt-in/opt-out options for AI services.
    5. Tailored messaging for different stakeholders.

Example: uCare.ai

  • Transparent AI use led to accurate hospital bill estimations.
  • Stakeholder communication, explaining AI usage, and facilitating feedback and concerns.

Regulatory Compliance

  • Importance of compliance with data protection, privacy, and other legal requirements.
  • Legal disputes can arise if compliance is not met.
  • Recommendation: Seek advice from local compliance and legal professionals before deployment.

Conclusion

  • AI's capabilities enhance effectiveness and reduce human errors.
  • Human centricity ensures AI outcomes are beneficial for human wellness.
  • Ethics and effective communication ensure unbiased and ethical AI deployment.