Data and Artificial Intelligence (AI) Ethics Navigator

The following contents are covered

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



Data and Artificial Intelligence (AI) Ethics Navigator test version

Get a first impression of the business analytics Data and Artificial Intelligence (AI) Ethics Navigator. This preview contains a number of questions from the full analytics.
Try it out now
Employees with hard disk and construction plans
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.

Start the preview of the Business Analytics now

Start the Business Analytics preview. It contains an overview of all dimensions of the analysis and offers you a first insight into the question structure at hand with one of many response scales.

Plan your next steps

Would you like to find out more? Talk to us about your current tasks and how this Business Analytics can support you in developing solutions for them. You can try out the Business Analytics free of charge first and see initial results straightaway. Plan your individual roadmap with KPMG Atlas!


We're looking forward to hearing from you.

Get in contact with us here to find out more about our service. We're looking forward to hearing from you to discuss your specific issues.

Contact us

* Required fields

Information on data processing can be found in the Data protection declaration

Try out the Business Analytics in full and free of charge now!

Have you already planned your next steps? Here you have the opportunity to perform an individual maturity level determination and receive an individual summary of the results immediately.

Off-the-shelf not for you?

Please contact our experts to create a non-binding custom-fit solution for your organization.

Would you like to find out more about our tailored service?

Explore Tailored

Advantages of Business Analytics Services

Benefit from our rapid and uncomplicated location determination for your current issues at any time by using our digital service.

KPMG expertise

Drawing on the specialist knowledge of KPMG experts, we navigate you through your relevant issues. 

Ad hoc assessment

Define your individual initial need for action in real time using the results report. 

Benchmarking

Obtain valuable benchmarking data to evaluate your own position. 

Related Services

Robotic Process Automation (RPA)

Robotic Process Automation (RPA)

Identify the potential of automation for your organization.

Explore now

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.