Responsible Artificial Intelligence (AI)

A Responsible Artificial Intelligence (AI) is a artificial intelligence (AI) that is developed and deployed with a commitment to ethical practice, legal practice, and socially beneficial practices.

• Context:
  • It can (typically) emphasize accountability, transparency, and fairness in AI systems.
  • It can (typically) be designed to reduce AI bias, increase transparency, and enhance end-user trust in AI systems.
  • It can (often) involve adhering to regulatory compliance and ethical standards.
  • It can (often) include mechanisms for human oversight and explainability in AI decision-making.
  • It can be essential in sectors like healthcare, automotive industry (e.g., self-driving cars), and customer service (e.g., chatbots).
  • It can be a focus of regulatory bodies and industry groups to ensure AI benefits society while minimizing risks.
  • It can involve collaboration between AI developers, policy makers, and end users to ensure responsible usage.
  • ...
• Example(s):
  • AI systems in autonomous vehicles designed with safety and accountability features.
  • AI applications in finance that provide transparent decision-making processes.
  • Microsoft has created its own responsible AI governance framework with the help of its AI, Ethics and Effects in Engineering and Research Committee and Office of Responsible AI groups.
  • FICO has developed responsible AI governance policies that include building, executing, and monitoring explainable models for AI, and using blockchain as a governance tool.
  • IBM employs an internal AI Ethics Board to support the creation of ethical and responsible AI across the organization.
  • ...
• Counter-Example(s):
  • AI systems that operate with minimal human oversight leading to ethical dilemmas.
  • AI used in surveillance without adequate privacy protections.
  • AI systems that operate without transparency or accountability.
  • AI deployments that fail to address privacy and security concerns adequately.
  • Unregulated AI Systems,
  • Black-Box Algorithms,
  • Bias-Promoting AI.
• See: AI Ethics, AI Governance, Sustainable AI, AI Bias, Transparency in AI, AI Security, Ethical Computing.

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References

2024a

• (ChatGPT-OpenAI, 2024) ⇒ Prompt: Summarize the concept Responsible Artificial Intelligence (AI) in 1- 2 paragraphs. ChatGPT (model:gpt-4-turbo-preview). Retrieved: 2024-03-03.
  • QUOTE: "Responsible Artificial Intelligence (AI) refers to the practice of designing, developing, and deploying AI systems in a manner that is ethically sound, socially beneficial, and within legal frameworks. It encompasses the principles of transparency, fairness, accountability, and safety, ensuring that AI technologies augment human capability without infringing on human rights or causing unintended harm. As AI technologies become more pervasive in society, responsible AI advocates for a proactive approach in addressing the ethical, legal, and moral implications associated with AI applications, from privacy concerns and bias in AI algorithms to the broader impacts on employment and societal structures.

    The concept underscores the importance of multi-stakeholder engagement, involving not just technologists but also ethicists, policymakers, and the public in discussions about AI governance. It pushes for strong ethical guidelines that can inform the entire lifecycle of AI systems, from design to retirement, ensuring that AI serves the common good and minimizes risks to individuals and communities. By integrating principles of responsible AI, developers and deploying entities aim to build trust in AI technologies, fostering their adoption in a way that aligns with human values and promotes a sustainable and equitable future.

2024b

• (Weir et al., 2024) ⇒ Charles Weir, Anna Dyson, Olamide Jogunola, Louise Dennis, and Katie Paxton-Fear. (2024). “Interlinked Computing in 2040: Safety, Truth, Ownership, and Accountability.” In: Computer, 57(1). doi:10.1109/MC.2023.3318377

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