Data and Artificial Intelligence (AI) Ethics Navigator

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The Business Analytics Ethics Compass for Data and Artificial Intelligence (AI) analyzes seven dimensions that are instrumental in determining the ethical maturity of your organization:


  • Focus on People
  • The Common Good
  • Understanding, Identifying and Balancing Competing Needs
  • Maintaining and Respecting a Person’s Freedom of Action
  • Do no Harm
  • Use of Ethical Best Practices to Protect Data
  • Data Quality and Integrity
  • Ethical Data Monetization

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Global investments in big data and artificial intelligence are making it increasingly important to understand the ethical challenges and implications associated with the introduction of advanced data analysis and automation solutions. KPMG’s Data and Artificial Intelligence (AI) Ethics Navigator has been developed to assess the maturity of data and AI business processes within your organization on the basis of ethical principles. Your individual maturity is evaluated using specific questions on eight underlying data and AI principles.

Use this business analytics to ascertain the ethical maturity of the entire organization, to identify areas where measures may be needed, to set priorities, and also to support the development of your ethics roadmap.

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Good to know

What are the ethics of AI?

The ethics of AI encompass all challenges arising from the use of artificial intelligence. With the number of problems significantly increasing in recent years, the ethics of AI have evolved as a new field for research and application. There are three important components to this: algorithm ethics, data ethics and moral judgment. Algorithm ethics address the ethical bases of an AI system, which need to be considered during the development of these algorithms. Data ethics involve the generation of data that is used for AI as data is the base for each AI system. Moral judgment focuses on the people who develop, use and design AI and how reflective they are in handling the data.

Profit orientation and morals often conflict with one another. Sometimes it is necessary to accept initial losses as a short-term investment in order to achieve profits over the long term. Ethical conduct is an investment in the value of trust, which pays off, for instance, through long-term cooperation. In times of continual new challenges, such as resource scarcity or threats to ecological foundations, long-term goals often lose their focus.

Where AI applications are used, consumers will increasingly question whether ethical principles are being adhered to. If this is the case, consumer acceptance will rise. In addition, consumers will be willing to publish positive reviews or to buy more products. By contrast, consumers will steer clear of producers exhibiting questionable, non-transparent approaches. Companies will also be aware of this development; they face the challenge of introducing AI systems quickly without jeopardizing the principles of ethics. According, ethical aspects already need to be considered right from the outset of development.

If you want to deal with artificial intelligence or use it, you need to be aware of the risks. Often users are concerned as large parts of controls are passed on to artificial intelligence systems and there is no person responsible in the case of damages. Further, large volumes of data are required to train algorithms, which means that data misuse cannot be ruled out. Transparency is another major point for criticism. AI decisions are generally not comprehensible and can therefore lead to wrong decisions. To counter this, researchers are already working on the issue of "explainable AI". An example that is often mentioned in this context is the "AMS algorithm" [job prospect profiling algorithm in Austria]. The job prospects of unemployed people are ranked into low, medium and high categories. Critics allege that the algorithm is not transparent, and that it is discriminatory and based on out-dated knowledge. In addition, decisions could potentially be based on outdated or erroneous information.