Algorithmic Traceability

Computer Algorithms are a set of instructions that solve specific problems. And more and more we’re relying on these algorithms to make decisions.

These algorithms are also in your organization. They’re busy trying to understand and predict your customer’s behavior.  People working at organizations are just like the rest of us.  Soon they start following the suggestions without even thinking. 

This happens so often that it’s extremely difficult to trace back all the steps the algorithm took in making its decision.  This is called the challenge of algorithmic decision traceability.

What is Algorithmic Decision Traceability?

Algorithmic decision traceability refers to the ability to track and understand the decision-making process of an algorithm.

It involves documenting how an algorithm arrives at a particular decision, including the data inputs, the processing steps, and the outputs.

This traceability is essential for ensuring that AI systems are transparent and accountable.

Why is traceability important in algorithmic decision-making?

Algorithmic decision traceability plays a significant role in guaranteeing transparency and accountability. It helps you understand how decisions are made

Traceability allows stakeholders to see the entire decision-making process. This includes the data used, the steps taken, and the reasons behind each decision. Without traceability, you can't check if the algorithm worked fairly and correctly.

Imagine a scenario where an algorithm denies someone a loan. Without traceability, it's impossible to determine whether the decision was based on accurate data or if there was a bias.

Furthermore, traceability is essential for improving algorithms. By understanding their decision-making processes, you can pinpoint areas that need improvement. 

Challenges to Algorithmic Decision Traceability

The decision traceability challenges are often referred to as a customer's “right of explanation.” What do you owe your customers after you’ve made a decision about them using an algorithm?

Think of it this way.  We know that companies like Meta and Google put people into many different advertising groups. These are called affinity groups.  They help these companies target customers based on their affinity with other customers.  So, if one person in the group likes a product, then it’s more likely other people in the group will like it as well.

But your organization might use the same techniques to put people into their own groups. You might decide that a customer is risk-averse or financially irresponsible. So that customer might be grouped as a high-risk loan candidate.  Your company might put this customer in the group, but the customer will no idea why they’re in this group or how to get out?

The ethical challenge is how much traceability there is in how these algorithms make these decisions. 

That’s because your company’s algorithms will make decisions about your customer based on these affinity groups. 

A customer might be denied a car loan, but have no idea how the algorithm made the decision.  They might pay more insurance, but have no idea what decisions went into making them a higher risk. 

When the algorithm is making the rules, you have to think about what right of explanation your organization owes its customers. 

That's why when you're working for an organization you have to think about the ethical challenges around making sure that your customer understands each of the steps that were taken when making your decision.

As the algorithms become more complex it becomes much more difficult to retrace your steps and explain these decisions back to your customer.

Main obstacles to achieving effective traceability

Facing challenges in achieving effective traceability in algorithmic decision-making involves tackling several key obstacles.

First, the complexity of algorithms can be overwhelming. Some are so complicated that understanding every decision path is nearly impossible without extensive expertise.

Second, proprietary algorithms present a significant obstacle because companies aren't always willing to share their inner workings. This makes it hard to understand where decisions come from.

Another major issue is data handling. You need to assess how data is collected, processed, and stored. If you don't carefully record data changes, tracking it later becomes very hard. Bias in data creates a big barrier. If an algorithm learns from biased data, finding and fixing issues without tracking is tough.

Moreover, a lack of standardized practices across different sectors makes it difficult to establish a uniform approach to traceability.

Finally, you have to deal with limited resources. Effective traceability requires time, financial investment, and skilled employees, which can be scarce in many organizations. 

What strategies can overcome the challenges of traceability?

Effectively overcoming the challenges of algorithmic decision traceability begins with simplifying algorithm design.

By creating straightforward, modular algorithms, you make it easier to understand and track their decision-making processes. This approach allows for clear documentation and segmentation, helping to pinpoint where and how decisions are made.

Next, employ transparent documentation practices. Keep detailed logs of all data inputs, processing methods, and outputs. This guarantees that others can review and understand the steps taken by the algorithm. Make sure to use standardized documentation formats to maintain consistency and facilitate easier reviews.

Another strategy is to implement robust version control. By tracking changes and updates to the algorithm, you can see how the algorithm has evolved over time. This can help you understand where any mistakes or differences in decisions came from.

The Importance of Traceability in Algorithmic Decisions

You've seen why traceability in algorithmic decision-making's important.

By documenting why you made design choices, where you got your data from, and the versions you used, you make sure everyone can see and understand the process. Even though dealing with complicated data and new technology can be tough, setting up audit trails and strong rules can help you overcome these challenges.

Finally, keeping track of things not only helps you avoid ethical problems but also makes people trust your computer systems more. That way, they become fairer and more dependable for everyone.

Frequently Asked Questions

What is the importance of ethics in algorithmic decision-making?

Ethics in algorithmic decision-making is important as it ensures fairness and accountability in the outcomes produced by artificial intelligence (AI) and machine learning systems. It helps to address biases and promotes responsible AI practices that can lead to more equitable decision support.

How can a literature review contribute to understanding ethics in algorithmic decision-making?

A literature review can provide a comprehensive overview of existing research on ethics in algorithmic decision-making. It allows researchers and practitioners to analyze past findings, identify gaps, and outline best practices that can guide the development of ethical AI systems.

What role does data collection play in ensuring ethical algorithmic decision-making?

Data collection plays a key role as it forms the foundation of machine learning models. Ethical data collection practices ensure that the data sets used are representative and free from bias, which is important for producing fair and responsible AI outcomes.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or LLMs. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and ethics.

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References:

1. https://journals.sagepub.com/doi/10.1177/2053951716679679

2. https://academic.oup.com/ppmg/article/5/3/232/6555119

3. https://jme.bmj.com/content/46/3/205