
AI Finance Flawed? How Blockchain & XAI Can Fix the Bias

Lim Qiaoyun
AI has been a game-changer across industries, and the transformation AI is bringing to financial services holds unparalleled potential for efficiency, personalization, and innovation. This technological revolution poses serious challenges, especially for bias. These AI algorithms require thoughtful design and oversight. Without it, they risk perpetuating and amplifying societal biases, which leads to discriminatory outcomes. DreamingCrypto is a multimedia exploration of how behavioral biases shape our financial decision-making—especially in the world of crypto. It investigates how blockchain and Explainable AI (XAI) technologies can drive increased transparency, accountability, and fairness.
This is a major contributor to AI bias in finance, demonstrating the fundamental data and algorithmic complexities below the surface. Biases can creep into all stages of AI development and deployment. They can impact important decisions, such as loan approvals and investment strategies. Recognizing these biases is the first step toward reducing their dangerous impact.
Anchoring bias is the most well-known type of cognitive distortion. It’s what happens when people over-weigh the first piece of information they see. Expertise bias is the fallacy of deference to those dubbed experts, without ever checking if they are indeed expert or not. Data bias occurs when models are built on incomplete or non-representative data, resulting in biased models. Leakage from the future is when you use future data to train your models creating artificially inflated and skewed results. Proxy bias is introduced if we trust correlating data points to make conclusions, even though they don’t have the same causal link. For instance, using the type of computer someone uses can unintentionally discriminate against certain groups.
The Impact of AI Bias on Financial Decisions
The impact of AI bias in finance can be considerable, with implications for consumers, industry players, and the overall health of financial markets. DreamingCrypto warns that there are serious dangers in allowing unregulated AI to make such important financial decisions.
Financial and Operational Risks
When bias infects AI, it has the ability to totally corrode the compliance of AI and best business performance, causing financial losses and catastrophic business failure. For instance, biased models of credit scoring can preclude worthy individuals from receiving loans—which curtails their potential and exacerbates disparities in wealth accumulation. In the context of investment management, biased algorithms may result in distorted asset prices, increased correlation, herding behavior or even market bubbles. These results occur in part due to AI’s propensity to exacerbate biases within investment decisions, resulting in systematic mistakes in investment choices.
Discrimination and Ethical Concerns
Biased AI can discriminate in hiring and promotion processes. This bias undermines qualified candidates from being selected and fosters inequities in the workforce. This creates adverse impacts to individuals and narrows the talent pipeline for organizations, constraining their perspectives and increasing their innovation risk. With the increasing use of third-party AI models comes significant data privacy risk, especially when those models are tuned with sensitive internal data. As these models are opaque, it’s hard to identify biases. This is particularly troubling given how hard we’re finding it to un-bias those biases.
Blockchain and XAI: A Solution for Fairer Finance
Luckily, technologies such as blockchain and Explainable AI (XAI) provide novel and encouraging ways to combat AI bias in finance. These technologies have the potential to improve transparency, accountability, and fairness in AI-driven financial systems. DreamingCrypto explores how.
Blockchain for Transparency and Accountability
Here’s how blockchain technology can help address AI bias with its unique features.
- Immutable Record of Transactions: Blockchain provides a secure, tamper-proof record of each transaction, making it difficult to alter or manipulate data.
- Transparency: All transactions on a blockchain are publicly visible, allowing anyone to view and verify the transactions in real-time.
- Decentralized Nature: Blockchain is decentralized, meaning no central authority controls transactions, reducing the risk of manipulation or corruption.
- Consensus Mechanism: The consensus mechanism ensures all nodes on the network agree on the state of the blockchain, preventing any single entity from altering data.
- Real-Time Settlement: Blockchain enables real-time settlement of transactions, reducing the need for intermediaries and increasing transaction speed.
These features make blockchain an ideal tool for logging AI model decisions, allowing regulators and stakeholders to trace how decisions were made and identify potential biases.
Explainable AI (XAI) for Understanding and Trust
Explainable AI (XAI) efforts have been oriented towards making AI decision-making processes more transparent and understandable. By shedding light on the decision-making processes of AI models, XAI can play an important role in detecting and addressing biases. XAI techniques can help identify which data points and features impact specific decisions the most. This will better empower developers to assess the fairness and relevance of those factors. This level of transparency is essential for fostering public trust in AI systems and making sure they’re deployed ethically and responsibly.
Real-World Examples
Some governments and nonprofits have already begun to use both blockchain and XAI to fight AI bias in finance.
FICO's AI Bias Detection Tool
FICO, a major credit scoring enterprise and the main supplier of the scores FICO itself, is on the leading edge of this push. Now, FICO leverages blockchain technology to write all AI model decisions into an immutable ledger, making it possible for regulators to trace how individual decisions—such as credit approvals—were derived. They describe their AI bias detection tool as being their most important. That’s why they have released it for free use by the entire industry.
We applied FICO’s AI bias detection tool to reveal biases in OpenAI’s GPT-4 Turbo large language models. The tool revealed that certain demographics of applicants required an additional 120 credit points above their white peers in order to receive approval. This was the case despite them having the same income, credit score and debt-to-income ratio. Industry and academics have begun to use it to spot harmful systemic biases across facial recognition datasets. This understanding will be important for creating the most beneficial facial recognition applications within cybersecurity, law enforcement, and customer service. The FICO application of blockchain technology will be an integral part of establishing trust in AI models.
Web3 Projects
From the decentralized science of XAI to smart charity, many Web3 projects are using blockchain and XAI to build fairer, more transparent financial systems. These projects address algorithmic bias in lending platforms. They challenge discriminatory practices baked into decentralized finance (DeFi) protocols. All of these projects utilize the transparency and immutability features of blockchain technology. Their hope, like ours, is to create a more inclusive and equitable financial ecosystem.
Conclusion
Unchecked, AI bias undermines the integrity and fairness of our financial systems. By embracing new technologies including blockchain and Explainable AI (XAI), the industry can better position itself to mitigate this risk. It’s this fundamental shift that has the potential to truly unlock AI’s power. DreamingCrypto is deeply curious about the emerging world of Web3. We need to be considerate and intentional about transparency, accountability, and ethical implications as we continue to propel innovation in a positive direction. Let’s work at putting these values first! With your help, we can build an AI-enabled future that brings power to the people and builds a more just financial ecosystem.