Case study ensuring accountability and fairness in AI driven loan approval systems.
The Finn Trust Bank Case study, accountability and automated decision making systems is integral to
ensuring that the decisions made by these systems are transparent, fair, and justifiable.
Consider the case of Finn Trust Bank, a large financial institution that recently implemented an AI
powered loan approval system to streamline its application process and enhance decision making efficiency.
One morning, an email lands in the inbox of John, the director of financial services at Finn Trust
Bank.
The email is from Emma, a small business owner who applied for a loan to expand her bakery but was
denied by the new AI system.
Emma is perplexed and frustrated because she has a solid credit history and a thriving business.
She demands an explanation for the denial and expresses her concern that the decision might have been
unfair.
John immediately senses the gravity of the situation.
He understands that the AI systems decision needs to be transparent and justifiable to maintain the
bank's credibility and customer trust.
He calls a meeting with his team, including AI developers, data scientists and customer service representatives,
to understand the rationale behind the loan denial.
As the team delves into the system's processes, they realize that the AI model relies on a complex
set of algorithms and vast amounts of data to make its decisions.
The system scored Emma's application low, but the exact reasons behind this score are not easily decipherable.
A classic example of black box problem in AI systems.
Here, the opacity of the system poses a significant challenge.
How can Fintrust Bank provide a clear and understandable explanation to Emma?
The first step towards addressing Emma's concern is enhancing the transparency of the AI system.
John suggests documenting the data sources, algorithms and decision making criteria used by the system.
The goal is to ensure that all stakeholders, including applicants like Emma, can understand the basis
of the decisions.
The team decides to develop explanatory tools like local interpretable model agnostic explanations and
Shapley additive explanations to offer insights into the decision making process.
During the analysis, the team discovers that the system might have used outdated or biased data, which
could have unfairly impacted Emma's loan application.
This brings up another critical question how can Fintrust Bank ensure that its AI system does not perpetuate
biases present in the data?
To tackle this, John advocates for regular audits of the AI system.
These audits aim to identify biases, errors, and other issues that might affect decision fairness.
Internal audits by the bank's data science team, coupled with external audits by independent experts,
can provide a robust mechanism for oversight and continuous improvement.
The European Union's General Data Protection Regulation mandates such auditing practices to ensure transparency
and compliance.
As the team continues to investigate, they consider the broader implications of accountability.
Who should be responsible if the AI system makes an error or produces an undesirable outcome, such
as denying a loan to a deserving applicant?
This question of responsibility allocation is complex and multifaceted.
John believes that responsibility should be shared among the developers who design the algorithms,
the bank that deploys the system, and regulatory bodies overseeing financial practices.
Each stakeholder has a role in ensuring that the system operates ethically and effectively.
For instance, in the event of a self-driving car accident, liability might involve the car manufacturer,
software developers, and the car owner.
Similarly for Fintrust Bank.
Clear legal and ethical frameworks are needed to delineate responsibilities and actions when issues
arise.
Furthermore, the team reflects on the ethical principles that should guide the AI systems operation,
ensuring fairness is paramount.
The team recalls a study revealing that facial recognition systems can exhibit biases based on race
and gender, leading to misidentification and unfair treatment.
Although Fintrust AI system is different, the principle remains the same.
Biases in data or algorithms must be addressed to ensure equitable decisions.
To mitigate biases, the team decides to use diverse training data and implement algorithmic adjustments.
Additionally, the bank adopts organizational policies for regular bias assessments and interventions.
These measures are essential to uphold fairness, justice, and respect for individual rights in the
bank's decision making processes.
While technical and organizational measures are crucial, the team also recognizes the importance of
legal and regulatory frameworks.
Governments and regulatory bodies can set standards for the ethical use of automated decision making
systems.
For example, the Algorithmic Accountability Act in the United States requires companies to conduct
impact assessments and address identified risks in their AI systems.
Fintrust Bank commits to complying with such regulations to ensure ethical governance.
Finally, the team acknowledges the need for public engagement and education.
Engaging with customers about the capabilities, limitations, and ethical implications of the AI system
can build, trust and align the system with societal values.
Educational initiatives can empower customers like Emma to understand and critically assess AI driven
decisions, fostering informed and active participation.
In conclusion, John and his team at Fintrust Bank embark on a comprehensive strategy to address accountability
in their AI powered loan approval system.
They enhance transparency by making the system's workings more understandable and accessible.
They establish robust auditing mechanisms to identify and correct biases and errors.
They clarify responsibility allocation among developers, the bank, and regulators.
They uphold ethical principles by addressing biases and ensuring fairness.
They comply with legal and regulatory standards, and they engage with the public to build trust and
promote informed participation.
By addressing these various aspects, Fintrust Bank ensures that its automated decision making system
operates transparently, fairly and justifiably.
Emma receives a clear explanation of her loan application denial, restoring her trust in the bank.
The bank's commitment to accountability not only upholds ethical standards, but also enhances its credibility
and customer satisfaction.