Gains:
- Ability to recognize risks of discrimination, bias and indirect discrimination in pricing and underwriting and test them in artificial intelligence output
- Ability to observe the right to explainability and objection within the framework of SEDDK regulations, KVKK and automatic decision-making principles
- Ability to apply human control and justification obligations in high-risk automated decisions
Insurance is a business that groups and prices risk; but this grouping has to be fair. Giving a higher premium or denying coverage to a person because of protected characteristics such as their gender, ethnicity, belief, region of residence is unfair discrimination and is prohibited. Artificial intelligence (software that learns from historical data and generates price and decision recommendations) poses a particular danger in this area: it can learn unfair patterns in historical data and unwittingly perpetuate or even hide discrimination. Moreover, it can do this not directly but indirectly (through a proxy variable). In this unit, you will learn how to recognize the risks of bias and discrimination in pricing and underwriting, how to test the artificial intelligence output for fairness, and how to observe the right to explainability and objection within the framework of SEDDK (Insurance and Private Pension Regulation and Supervision Agency), KVKK and automatic decision-making principles. This unit is not a legal consultancy; It explains the general principles, in any concrete case, be sure to consult the legal/compliance unit.
Where do prejudice and discrimination come from?
AI bias arises from three main sources:
- Data bias: The model learns about unfairness in historical data. For example, if a certain group has been systematically given high premiums in the past, the model continues to consider this as "normal".
- Surrogate variable (proxy): Even if the model does not directly use the protected feature (gender, origin), it discriminates indirectly by using a variable that is strongly related to it (neighbourhood, name, shopping pattern).
- Sampling bias: Because some groups are underrepresented in the data, the model produces unreliable and unfair judgments about them.
There are two types of discrimination. Direct discrimination: blatant use of a protected characteristic. Indirect discrimination: when an apparently neutral criterion disproportionately affects a particular group. The second is more insidious because it occurs without intention and is difficult to detect.
Caution: The defense that "the model is based on statistics, so it is objective" is wrong. Statistics also include past injustice; The model does not legitimize it, it perpetuates it. Fairness is something that needs to be actively tested.
Testing output for fairness
Test the AI output (price recommendation, rejection reason, risk score) with three questions for fairness:
- Is the reason legitimate? Is the decision based on criteria that have a real and justifiable relationship to the risk, or on the protected property or its proxy?
- Is there a proxy? Could one of the variables used (neighborhood, name, occupation, marital status) be strongly associated with a protected characteristic?
- Can it be explained? Can you explain the decision to the client and auditor in a clear and defensible manner? “The model said so” is not a statement.
If a justification does not pass these three tests, it is rejected and the decision is based on legitimate criteria.
Tip: Test the rationale for a rejection or high premium with this sentence: "Will I be embarrassed if I explain this to the client and an auditor?" A shameful or indefensible justification (neighborhood, name, origin) should not be used.
Legislative framework: SEDDK, KVKK, automatic decision
The use of artificial intelligence in insurance is subject to various regulations. Main principles:
- SEDDK principles: Insurance activities should be carried out in a fair, transparent and consumer-protective manner; Pricing must be justifiable and there must be no unfair discrimination.
- KVKK: Personal data must be processed in accordance with the law, limited to the purpose and securely. Special data, such as health, are subject to the highest protection.
- Automated decision-making principles: In decisions that are based solely on automatic processing and significantly affect the person (for example, a rejection made by artificial intelligence alone), the person; The rights to object to the decision, to request human intervention and to learn the rationale of the decision come to the fore.
Therefore, explainability, human control, and justification are essential in high-impact decisions. The unjustified decision of a “black box” model is both uncontrollable and in conflict with these rights.
Step by step: Fair and harmonious decision
- Identify protected properties. Gender, origin, belief, health, region of residence, etc.
- Do a proxy scan. Question the relationship of the variables used with protected properties.
- Put the justification to the test of legitimacy. Is there a real relationship with risk? Can it be explained?
- Add human control. Let a human review the automatic result in a high-impact decision.
- Document the rationale. Record what legitimate criteria the decision was based on.
- Know the way to appeal. Provide the customer with the right to object, human review and justification.
three mini cases
Case 1 — Rejecting the proxy. A housing pricing model suggested a systematically high premium for a particular zip code. The expert examined: the postcode overlapped with an area with a certain ethnic concentration; this was a proxy variable. When the actual risk (building age, structure type, damage history) is broken down, most of the increase is no longer justified. The price has been reestablished based on legitimate risk criteria.
Case 2 — Underrepresented group. For a healthcare product, the model offered an excessively high premium to a small age group. Reason: this group was so underrepresented in the data that a few high damages made the entire group appear "at risk". The expert performed a credibility (statistical reliability) correction and documented that the recommendation was unreliable; Unfair raise was not applied.
Case 3 — Right to object. An application was automatically rejected with an AI-powered score. The customer objected. As part of the adaptation process, the decision was re-examined by an expert; The score appeared to be based on missing data (mismatched past damage). The decision has been corrected. Without human control and the right to object, unfair rejection would be permanent.
Four copyable prompts
1) Proxy and discrimination scanning:
Check the following pricing/decision justification for fairness: [justification]Look for: is the protected characteristic (gender, origin, belief, health, region of residence) used directly or indirectly (proxy: neighbourhood, name, profession, etc.)? For each questionable variable, explain why it could be a proxy and suggest a legitimate alternative.
2) Justification legitimacy test:
Test the following denial/high bonus rationale with three questions: [justification]1. Is there a real and justifiable relationship to risk?2. Is it based on protected property or its proxy?3. Can it be explained to the client and the auditor? Answer each question clearly; Mark the reason that does not pass the test as "REJECT".
3) Explainable justification draft:
Draft an articulated rationale to the client for the decision based on the following VERIFIED, legitimate risk criteria: [legitimate criteria]Tone: respectful, clear. Using protected property/proxy. State the customer's right to object and request human review. This is a draft; expert confirms.
4) Human inspection checklist:
Create a human audit checklist for a high-impact automated decision:Decision type: [rejection/high premium/collateral constraint]List: qualified expert reviewing, data checked, fairness/proxycontrol, justification document, customer appeal notice, trail record.
Weak prompt / Strong prompt
Weak: "Determine the most profitable price with this data, and write the justification."
Problem: Fairness, proxy and explainability are not respected; the model can perpetuate and justify past discrimination.
Strong: "Check this justification for proxy and discrimination; flag points that rely on protected feature or its proxy and replace with legitimate risk criterion. Outline an explainable justification."
Why it's good: Discrimination is actively tested, the decision is based on legitimate and explainable criteria.
comparison chart
Subject
risky approach
right approach
Model output
"Statistics is objective"
Actively test justice
Variable selection
Use every data
Extract proxies
justification
"That's what the model said"
Legitimate, explainable criterion
High impact decision
fully automatic
Human supervision is mandatory
Customer right
Not reported
Objection + human review + justification
Common mistakes
- The "statistics is objective" defense. Justifying past injustice.
- Ignoring the proxy. Indirect discrimination based on neighborhood/name/profession.
- Unreliable decision for underrepresented group. Assuming that the rate obtained from a small sample is accurate.
- Black box decision. Implementing an automatic decision for which the reason cannot be explained.
- Not recognizing the right to object. Not giving the customer the right to human review and justification.
In summary
Artificial intelligence can learn discrimination in historical data and continue it indirectly (via proxy) in pricing and underwriting. Actively test fairness: identify protected properties and proxies, test justification for legitimacy and explainability. SEDDK, KVKK and automatic decision-making principles; Fair pricing requires explainability, human review and the right to object. Human control is essential in high-impact decisions. In the specific case, consult the legal/compliance department; Discrimination is the red line.
Application task
Write a list of 10 variables a pricing model uses (actual or example). For each, answer the question "could this be a proxy for a protected property"; Flag the suspicious ones. Then put a sample rejection justification through legitimacy test #2: does it pass the test, and if not, how do you replace it with a legitimate criterion?
checklist
- [ ] I have specified protected properties.
- [ ] I scanned the variables for proxy.
- [ ] I put the justification to the test of legitimacy and explainability.
- [ ] I conducted reliability checks for underrepresented groups.
- [ ] I added human control in the high impact decision.
- [ ] I documented the rationale and gave the customer the right to object/human review.
- [ ] When in doubt, I consulted the legal/compliance department.