In our previous article, we covered the tips for using generative AI efficiently and the art of prompt engineering. This week, we're tackling another critical aspect of working with AI: the bias problem. Unlike the phenomenon of hallucination, bias means there is a certain directional error or partiality in the content of the AI's answer. Biases embedded in an AI model's language can sometimes be difficult to detect, but can be extremely problematic in terms of their consequences. So what is bias, how does it arise, and what can we do as lawyers to overcome this problem? We'll address these in this article.

What Is Bias?

Bias, in general terms, is when an AI system shows systematic partiality or tendency to discriminate. AI bias arises as a result of the model being trained or designed in a way that inadvertently discriminates against people or certain groups. In other words, it's the emergence of a certain perspective, stereotypes, or unbalanced approaches in the answers given by the AI model. This bias can be seen as a reflection error resulting from patterns in the data the model learned, rather than intentional hostility. As a result, discriminatory or biased statements may inadvertently appear in the model's outputs.

If we think in legal terms, bias in AI can be likened to the unconscious biases of a judge or prosecutor. Just as a judge's past experiences or social conditioning can unwittingly direct their decisions, AI can also display a biased perspective based on the weights in the texts it was trained on. For example, if the vast majority of texts the model was trained on reflect the views of a particular culture, gender, or ideology, the model may center this perspective when answering questions.

How Does Bias Arise in AI Models?

Bias can sprout at different stages of the development process of AI systems. There is a risk of bias forming in three main sources:

• Training Data Bias: Imbalances or stereotypes already present in the large datasets the model learns from are transferred exactly to the AI. For example, an AI algorithm Amazon used for hiring in 2015 became discriminatory against female candidates because it was trained on resumes from the past 10 years. Because most of the resumes in the dataset came from men, and the algorithm learned the "ideal employee" profile in favor of men. Similarly, facial recognition systems can fail to recognize dark-skinned individuals when trained only on photos of light-skinned people; this is a typical result of data-driven bias.

• Algorithmic Bias: During the model development phase, the developer's biases or faulty assumptions are inadvertently reflected in the model. For example, choices made in the design and data processing method of the COMPAS risk assessment algorithm used in criminal justice resulted in the outcome containing racial bias. COMPAS disproportionately showed black defendants as high-risk when predicting a defendant's likelihood of reoffending; it produced twice as many "false positives" compared to white defendants. This situation resulted from a bias in the algorithm's mathematical formulation or data processing method. Algorithmic bias, although often unintentional, ultimately leads to systematic errors that are unjust to a particular group.

• Human Interaction and Cognitive Bias: AI systems can also gain bias as they interact with users or are shaped by human feedback. For example, Microsoft's chatbot named Tay, launched in 2016, was programmed to improve itself by interacting with Twitter users. However, when malicious users had Tay repeat racist and misogynistic statements, the bot quickly adopted these discourses and started producing similar hate speech. Similarly, when large language models are trained with feedback from human moderators, the cultural or political perspectives of those moderators and data sources can seep into the model. Indeed, OpenAI stated in its own help documentation that ChatGPT is not completely unbiased and particularly tends toward Western views and the English language. The model can easily adapt to users' strong opinions during dialogue and reinforce them; this can create a cycle that confirms existing biases.

In short, AI works on the principle "what goes in comes out": If the data or rules presented to the model are biased, the resulting answers will inevitably be biased too. This situation can lead to discriminatory or erroneous suggestions, even if unintended.

Why Is Bias Important in Law?

The foundation of the concept of justice rests on the principles of impartiality and equality. When a lawyer or judge acts with conscious or unconscious biases, the right to a fair trial is damaged. Similarly, as the use of AI tools becomes widespread in the legal field, these tools carrying similar biases can have serious consequences. Here are points lawyers should pay attention to regarding bias:

• Unfair Recommendations: If the AI model you use as a legal research assistant is biased, it may offer you incomplete or misleading suggestions when evaluating your client's situation. For example, if most of the legal texts the model was trained on are from past years and light penalties were given in cases of violence against women at that time, an AI tool that captures the "average" may respond to a similar question with a lower sentence suggestion. This may conflict with current legal approach and social sensitivity.

• Biased Language in Client Communication and Petitions: Lawyers have begun using AI tools to prepare petition drafts or legal texts. If there is a subtle bias in the model's language, biased or discriminatory expressions may inadvertently appear in the prepared draft.

• Misguidance and Responsibility: A biased AI can cause serious misguidance in the advice to be given to the client. Let's say you used the model for consultation on KVKK (personal data protection). If the model makes an erroneous and dangerous generalization like "Such violations generally go unpunished" based on the majority view in the training set, your client may have a false sense of security. Whereas perhaps very serious sanctions are involved in current practice. Since legal responsibility always rests with the lawyer, blindly adopting the biased or outdated advice of AI is a professional risk. Indeed, international bar reports emphasize that responsibility for errors arising from AI tools cannot be removed from the lawyer. If the client is harmed in an error caused by bias, saying "but the AI said so" is not an excuse. However, we will address this in a later article.

• Risk in Judicial Processes and Evidence Evaluation: In some countries, judges are also discussing benefiting from AI as decision support systems. If such a system is biased (for example, if it gives a higher risk score against a particular ethnic group), the principle of fair trial is seriously damaged. Although the use of AI in the judiciary in Turkey is still limited, it has already begun to be used in some areas, and in the near future, AI tools may come up in topics such as summarizing judicial decisions or precedent case law advice. At this point, lawyers will need to evaluate these tools with awareness of algorithmic biases. The demand for transparency will gain importance: questions such as "What data does the algorithm that reaches this conclusion rely on, does it systematically put a group at a disadvantage?" should be asked. For example, as in systems like Leagle, what data the results are based on should be clearly shown.

What Should Be Done to Overcome the Bias Problem?

Creating a completely unbiased AI may be an impossible goal, but managing the bias problem and reducing its effect is definitely possible. Here are some measures and strategies that lawyers and AI developers can take on this:

• Critical and Continuous Supervision: When you get a response from AI, don't immediately accept it as correct. Passing the content through a critical filter is the first rule. If the answer carries a negative implication towards a particular group or seems excessively one-sided to you, immediately raise alarm bells. If necessary, ask the model to use more neutral language with a second prompt or have it address the topic from a different angle. For example, you can give a command like "Rewrite your answer neutrally, showing arguments both for and against."

• Various Sources and Comparison: Always compare information or analysis from AI with reliable sources. AI tools can sometimes produce erroneous or fabricated judicial decisions; therefore, using verified legal databases like Leagle is more reliable, especially in precedent case research. Similarly, if you think the model responded with a single perspective, look at other sources that offer alternative perspectives. For example, if the model only defends one view on a controversial topic in criminal law, manually research the opposing view and add it to the equation. AI is an auxiliary tool, not the sole authority. Not making it the first and only source is critical to reducing the bias effect.

• Use Prompt Engineering: The prompt engineering techniques we explained in detail in our previous article can also help reduce bias. By asking the model the right questions or giving it certain roles, you can get more balanced answers. For example, requests like "Evaluate from a more technical perspective" or "Explain taking into account the views of different judicial authorities" help prevent the model from giving a one-sided answer. Also, if you think the model is over-weighting a particular culture or language, diversify the context in the prompt: like "Evaluate separately in terms of US and Turkish law." This way, the model's response expands to include multiple perspectives.

• Pay Attention to Model and Data Selection: If you have the opportunity to use multiple AI tools or models, prefer those that perform better on bias. For example, according to OpenAI's statements, GPT-4 has reduced the tendency to produce unwanted and inappropriate content by 82% compared to GPT-3.5. More advanced models have made progress not only in information quality but also in security and ethical filters. Still, rather than completely trusting a model just because it's "less biased," always use human supervision on sensitive topics. Don't forget that general-purpose models like ChatGPT have a high hallucination rate (tendency to produce non-existent information or precedent cases). If possible, consider developing customized AI models like Leagle for use in your legal work. A model trained with your own institution's datasets will provide results appropriate to your needs and controlled compared to a general-purpose model – the likelihood of unexpected surprises regarding bias also decreases.

• Awareness and Training: Finally, the legal community needs to adopt a continuously learning attitude towards AI. It's a fact that we cannot completely eliminate the bias problem. Therefore, our most important weapon in fighting it is awareness. In professional training, potential biases of AI tools should be taught to junior lawyers and young legal professionals; discussions should be held on real examples ("case studies"). For example, in a training session, having a large language model (LLM) answer the same scenario in different ways can show how even small changes in the question form affect the bias in the answer. In guides published by bar associations, lawyers are advised not to use AI outputs without verifying their accuracy and impartiality. Internalizing such principles is the key to using AI responsibly.

AI brings speed and efficiency to the legal world while also raising new ethical and professional responsibility questions. The bias problem shows that not only the technical but also the human and social aspects of these technologies need to be carefully addressed. It must be remembered that impartiality is the responsibility not only of algorithms but also of the lawyers who use them.

The way to safely use AI in legal practice goes through awareness, critical thinking, and continuous supervision. No matter how advanced the models' outputs are, the final judgment should always be with humans.

Next week, we'll address another trap that AIs frequently fall into, the "hallucination" problem. So how can an AI "make up" a topic it knows nothing about? Why can distinguishing fact from fiction be so difficult? And as lawyers, how can we deal with these "hallucinatory responses"? You'll find answers to all these questions in our next article.