AI in general 1, 2017

Responsible AI and Robotics

United Kingdom




Liability, Transparency, Bias, Data protection,


Accenture’s ethical framework highlights the importance of predictability and trust, which are seen as interrelated factors necessary for AI’s success. It underlines the role of developing flexible and robust legal frameworks, however, Accenture’s view is that such frameworks must be accompanied by an ethical code, developed collaboratively by the government, business, academia and society more widely. Such a code must provide guidance on 7 key ethical challenges:
  • Decision making and liability - who is liable when systems make mistakes?

  • Transparency - when must AI systems (not) explain their actions to humans?

  • Bias - stop systemic bias and promote core values of equality, diversity and lack of discrimination

  • Alignment with human values - what should AI’s core ethical values be?

  • Data protection & IP - how to balance data protection with the promotion of innovation?

  • Social dislocation - what obligations do AI deployers have to mitigate the resulting social consequences?

  • Cybersecurity - ensure strong protection against hacking.

  • [/ul]
    Accenture identifies 5 areas where such a code should apply:
  • Autonomous vehicles

  • Healthcare

  • Finance

  • Energy

  • Defence

  • [/ul]
    The framework proposes the establishment of an AI Advisory Body to issue guidance to industry and regulators. In collaboration with key stakeholders, core ethical principles should be developed, along with sector-specific codes for the above-identified 5 areas.

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