Our next data science ethics bookclub is on AI and bad credit: the impact of automation on financial inclusion. You are welcome to pick from this reading list, depending on your interest and the time you have:
- **Blog: ‘** We Didn't Explain the Black Box – We Replaced it with an Interpretable Model’, about the FICO explainable credit score competition winner here
- Academic article on fairness in credit risk evaluation, ‘Context-conscious fairness in using machine learning to make decisions’ here
- Government paper : The Centre for Data Ethics and Innovation look at AI and Personal Insurance here
- News article on how companies can use data with low levels of regulation, ‘The new lending game, post-demonetisation’ here
- Academic article on discrimination in consumer lending here
Note: Questions are based on [CDEI meeting] ](https://twitter.com/peterkwells/status/1178597424669089792/photo/1)
Warm up
- Which of the papers from the recommended reading list did you read? What were your main takeaways?
- What are the main benefits of data science & AI into the financial
sector? Prompts (if conversation is slow:
- More efficient financial markets
- Increased access to financial products e.g fin tech innovation, or existing finance services
- Faster access to financial products
- Personalised services e.g. AI powered chatbots
- Detecting vulnerable groups earlier to provide support
- Better detection of economic crime- fraud/money laundering
- Increased detection of cyber threats
- Increase finance services efficiency
- Better risk assessment therefore low costs for customers
- Efficient compliance (regulatory technology)
- Who are the beneficiaries? Which group benefits the most
Negative consequences?
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What do you think some of the major risks within financial services sector arising from the application of data driven technology and AI? Prompts (if conversation is slow:
- Cyber attacks
- Extreme market movement (algorithms used to automate trade decisions) & difficulties monitoring algorithmic trade (black box)
- Lack of transparency
- Lack of explainability
- Loss of public trust in financial systems
- Use of non traditional finance data e.g social media encroaches on privacy
- Digital exclusion- what about people who don't use digital- they don't create lots of data!
- Preferential access to people who are willing to give lots of data e.g. provide car/fitness sensors to insurers
- Bias due to use of historical data to predict forwards
- Consumer disempowerment- asymmetric power as financial services know more about a customer than the customer itself
- Regulators unable to keep up with AI due to lack of resources
- Algorithmic collusion (outside of the financial institution)
- Increased surveillance of finance workers
- Deskilling of financial workers/ workers have excessive trust in algorithmic recommendations
- Data monopolies
- Fear of potential risks reduces AI takeup which could be a benefit
- Excessive data retention
- Increased inequality
- Non-alignment with societal goals
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How do these risks arise?
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Which groups are most affected and how?
General discussion question
- Consider yourself/family/friends as a consumer (e.g. for credit, car insurance, health insurance). What data would you share? What would be your concerns about its use? What would you want to know about a company's decision (explainability etc)? And - how should this be regulated/controlled? How have the reading materials and discussion so far changed how you thought about any of this?
Governance
- What governance is or should be in place to mitigate for negative
consequences?
- Legislation and regulation
- Technical solutions (can lead to discussions around fairness- see Michelle Lee's article)
- Soft governance (standards and codes)
- Anything else?
For tweets from the evening see here.
How do we leverage the possibilities of AI for greater financial inclusion?