The Need for Transparency And Accountability in Algorithmic Decision Making

The need for transparency and accountability in algorithmic decision making is on the rise. This is so because more organizations rely on machine learning algorithms for decision-making in many aspects of the organization.

The trend towards algorithmic decision-making is driven by the availability of more volumes of fine-grained data and new developments in machine learning. Using statistical data, algorithmic decision-making can determine what used to be left for managers. For example, determining customers’ creditworthiness and fraud, to name a few. Today,  organizations use machine learning to make such decisions. Its use has permeated industries like marketing, employment screening, and insurance eligibility screening.

 The market research firm IDC projected that the global AI market will reach a size of over half a trillion U.S. dollars by 2024. Precedence research suggests the market will grow to over 1.5 trillion U.S. dollars by 2030. ~ Statista

This article will shed light on the need for transparency and accountability when using machine learning algorithms to make decisions that affect people’s lives and livelihoods. But first, let’s discuss algorithmic decision-making and how it can be a problem.

What is Algorithmic Decision Making and Why is it Important to Understand its Use?

Algorithmic decision-making is the use of machine learning algorithms to analyze large amounts of personal data to infer correlations or derive information considered useful for making decisions. For instance, the financial sector uses algorithms to automate trading decisions and detect investment opportunities for clients.

This technology is used frequently for marketing, insurance eligibility, and employment screening, in addition to its historical use for credit determinations. It is also used in the public sector and the criminal justice system to make decisions for probation and sentencing.

How Does Algorithmic Decision Making Become a Problem?

Algorithmic decision-making can become problematic when bias is introduced into the process, sometimes becoming more discriminatory than human decision-making. The potential of algorithmic decision-making to increase opacity, information, power asymmetry, and discrimination is a huge concern. It can be challenging to determine when and how these machine-learning algorithms might bring bias into decision-making.

These problems create the need for transparency and accountability in how the algorithmic was used for decision-making. More researchers are calling for organizations to reveal and make transparent their algorithmic process. Specifically, the call is for organizations to become more transparent, accountable, and demonstrate fairness in how their algorithms are used to make decisions that impact people’s lives

Let’s talk about transparency, accountability, and fairness in algorithmic decision-making.

• Transparency in algorithmic decision making refers to how well users are told they are the targets of automated judgments and how well they understand the decision-making process behind such conclusions.

• Accountability in algorithmic decision making refers to how much human oversight and modification of the algorithm is possible and who is responsible for mistakes and liable to pay for the damage.

• The fairness of algorithms expresses a desire for impartial decision-making using objective factors. It lies where machine learning and ethics converge. Researching the root causes of bias in data and algorithms is a part of this field.

Problems with Algorithmic Decision-Making

While machine learning algorithms are a viable approach to reducing human prejudice in judgment, they can have a detrimental effect on the lives of millions of people if the data fed into the system is already tainted.

Algorithmic decision-making may pose the following dangers:

1.   Discriminating Judgments

The technological aspects of algorithms can also cause discriminating judgments, such as the member of protected classes inadequately represented in the input data that train the algorithms, or previous discriminatory actions may taint them. The results can lead to unintentional data duplicates or may exaggerate biases from the past.

2.   Societal Reflection

Algorithms are trained to generate predictions based on the data provided to them and the patterns they deduce. The prejudices that are present in our culture, both overt and covert, are reflected in the practices that algorithms find.

3.   Selective Labeling

Human prejudices are acquired over time through societal integration, cultural agglomeration, media impacts, and other factors. The algorithms that work on learned biases, if provided the biased database, will provide partial results, but they are not initially prejudiced.

How Transparency and Accountability In Algorithmic Decision Making Solves The Problem?

Algorithmic accountability strongly emphasizes algorithms and their transparency, as these factors hold the key to accountability. Making algorithms transparent might aid in holding human actors responsible, but focusing solely on algorithms is the wrong road to accountability.

Ensuring Unbiased Decision-Making

To tackle the increasing complexity, accountable algorithmic systems have much more significant data processing capabilities than are humanly capable as they keep account of all pertinent information and rule out non-relevant elements. That makes the data-driven decision-making process unbiased and more transparent.

Quick Data Evolution

Transparent machine learning algorithms also enable speedy, even split-second, decision-making by allowing organizations to process and evaluate data more quickly than ever before.

Better Organizational Management

Implementing the accountable ML algorithm in different public and private sectors will protect employees’ data, maintain the organizations’ reputation, and evade pricey corrective actions by making quick decisions about successful rehabilitation.

Organizations Adopting Accountable Algorithmic Decision-Making Technology

Artificial intelligence systems and machine learning algorithms are increasingly used in the public and private sectors to automate simple and sophisticated decision-making processes. Here, we will give examples of two organizations making efforts for accountable algorithmic decision-making.

1. IBM is one of those organizations making efforts to enhance the transparency of algorithmic decisions. The concept of AI FactSheets was proposed by IBM Research AI  to improve trust in AI by facilitating governance and enhancing transparency. Moreover, IBM has also introduced reinforcement learning to address problems within decision-making.

• Another example of an organization addressing algorithmic risks is Deloitte. Its digital ethics is aimed at bringing transparency and ethics into algorithmic decision-making and artificial intelligence.

Final Words

Algorithmic decision-making technology is swiftly seeping into our lives. However, the objectiveness of its decisions is still arguable, as machine learning-based solutions sometimes reinforce human biases that are even more difficult to spot. Given the widespread use of algorithmic decision-making systems, we must focus on the rigorous regulation of these technologies to save time.