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Glossary

Algorithmic Accountability: Responsible AI

Algorithmic accountability: who answers for automated decisions, what the GDPR and EU AI Act require, and how companies can demonstrate responsibility.

Definition: Algorithmic accountability is the principle that organisations stay responsible for what their algorithms and AI systems decide and cause. They must be able to explain how a system reaches its results and who is in charge of it. They must also show how errors are found and fixed. You cannot hand this responsibility off to the software or its vendor.

What accountability means in practice

Algorithms often help make decisions about people. Examples are job applications, credit limits, insurance premiums, shift plans or which customer requests come first. As soon as that happens, one question comes up: who answers for the outcome? Algorithmic accountability answers it with four building blocks:

  • Ownership: Each system has a named person or role in charge. This owner decides when the system is used, changed or shut down.
  • Traceability: Purpose, data, logic and limits are documented. You can trace single decisions later, for example through logs.
  • Auditability: Internal or external reviewers can test the system for errors, bias and compliance.
  • Redress: People affected by a decision have a way to challenge it. The organisation is able to correct wrong outcomes.

The concept is not the same as explainable AI. Explainability is a technical aid. Accountability is a principle for how an organisation works. Even a model that is easy to explain is of little use if nobody is in charge of checking the explanation.

Where companies typically fall short

Accountability rarely fails for lack of good will. It fails for lack of structure. Common gaps include:

  • A bought applicant tracking tool ranks candidates in advance, but nobody in the company knows the criteria.
  • Business units switch on AI features in existing software. IT, data protection and management do not know about it.
  • "Human review" is just one click that accepts the system's suggestion without a look (automation bias).
  • There are no logs, so you cannot trace a disputed decision later.

The risks are real:

  • discrimination claims under the German General Equal Treatment Act (AGG)
  • GDPR access requests that you cannot answer
  • damage to your reputation when faulty automation becomes public

Legal framework

GDPR

Article 5(2) GDPR clearly makes the controller accountable. Article 22 gives people the right not to be subject to a decision based only on automated processing that has legal or similarly significant effects. Exceptions exist. If one applies, safeguards are needed, such as the right to have a human step in. Under Articles 13 to 15, people must get meaningful information about the logic involved.

In its 2023 SCHUFA ruling (C-634/21), the Court of Justice of the EU made one point clear. A score alone can fall under Article 22 if it decides the outcome of a third party's decision. Where the risk is high, you also need a data protection impact assessment under Article 35.

EU AI Act

The AI Act (Regulation (EU) 2024/1689) turns accountability for high-risk AI into concrete duties:

  • risk management (Art. 9)
  • data governance (Art. 10)
  • technical documentation (Art. 11)
  • record-keeping (Art. 12)
  • transparency towards deployers (Art. 13)
  • human oversight (Art. 14)

Companies that use such systems have their own deployer duties under Article 26. These include skilled oversight staff and keeping logs. Under Article 86, affected people gain a right to an explanation of certain single decisions. AI used in employment, for example to screen applicants, is listed as high-risk in Annex III. The Digital Omnibus has been in force since 27 July 2026. Under it, these duties apply to Annex III high-risk systems from 2 December 2027.

Putting accountability into practice

  1. Create an AI and algorithm register: Record every system that makes or prepares decisions about people. Include bought software with AI features.
  2. Assign owners: Each system gets a business owner with clear authority. This includes the right to switch it off.
  3. Classify risk: Assess the impact on the people affected. Map each system to the EU AI Act risk categories. ISO/IEC 23894 gives you a method for this.
  4. Make human oversight real: Reviewers need time, information and the right to decide differently. Measure how often they actually change suggestions.
  5. Document and log: Record purpose, data, tests and changes. Keep decision logs for an appropriate time.
  6. Check fairness regularly: Compare outcomes across relevant groups. This helps you spot systematic disadvantages early.
  7. Set up a complaints channel: People must know whom to contact. They must get a response within a reasonable time.

An AI management system under ISO/IEC 42001 brings these elements together in a framework that can be certified. It also fits well with an existing ISMS under ISO/IEC 27001.

Conclusion

Automation does not change one fact: a person in the organisation stands behind each decision. That is what algorithmic accountability means. Define ownership, documentation and redress early. This meets GDPR and AI Act duties and builds trust with customers and staff. SecTepe helps set up AI governance that works hand in hand with data protection and information security.

Frequently asked questions

Are we responsible if the AI comes from a software vendor?

Yes. Under the GDPR, you remain the controller of the processing. Under the AI Act, you have your own deployer duties. The vendor must give you the information you need, so make sure your contract requires it.

Is it enough for an employee to confirm the AI's decision?

Only if the review is real. A formal sign-off without a real look at the case does not count as meaningful human involvement.

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