Algorithmic Accountability and Audit is a service at the intersection of modern law and technology, aiming to solve the "Black Box" problem. When companies use algorithms to make significant decisions (loan approvals, insurance pricing, hiring), there is a risk that these algorithms may be biased, opaque, or unfair. Often, even the system creators do not know exactly why the AI made a specific decision. Algorithmic accountability ensures that automated systems are fair, explainable, and lawful. This is particularly important in the context of Georgia's new "Law on Personal Data Protection," which grants citizens the right to receive an explanation of the logic behind automated decisions.
Our service combines legal and technical expertise to audit your algorithms. The service includes:
- Algorithmic Impact Assessment: Preliminary research on how the system will affect human rights and what risks it entails.
- Bias Testing (Bias Audit): Checking the system for discriminatory patterns (e.g., differential treatment based on gender, age, or ethnicity).
- Explainability Statement: Creating a legally sound document that explains to the user in plain language how the algorithm works.
- Compliance Audit: Checking the system against anti-discrimination laws and data protection regulations.
- Transparency Policy: Developing internal procedures for monitoring algorithm performance and upholding ethical standards.
Real-world examples show why this is necessary. A bank's credit scoring algorithm rejects women for loans more often than men, despite similar financial status. Without an audit, the bank might not even notice this discrimination, creating a risk of lawsuits. Second example: An insurance company uses AI to calculate premiums relying on obscure data (e.g., social media activity). This could be considered a violation of consumer rights if the logic is not justified. Third case: An online store uses dynamic pricing that offers higher prices to certain users based on their device type. This also requires legal justification.
In Georgia, this field is regulated by the Law on Personal Data Protection (Article 24 — Automated Decision Making), which obliges the data controller to explain the decision-making principle to the subject. The Law on the Elimination of All Forms of Discrimination also applies, prohibiting any form of (including algorithmic) discrimination. The Law on Consumer Rights Protection demands information transparency.
The audit process begins with "White Box" or "Black Box" testing. Experts analyze datasets and model outputs. If bias is detected, a "Mitigation Strategy" is developed, such as data balancing or adjusting algorithm parameters. As a result, the company receives an "Algorithmic Trust Certificate" or a compliance report.
Legal.ge is a unique platform giving you access to multidisciplinary teams (lawyers + data scientists). Algorithmic accountability is not just a technical issue; it is a matter of trust and legality. Ensure your AI systems are fair and transparent with the help of Legal.ge.
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