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Can we trust AI as a decision maker?

Updated: Oct 20, 2023

The evolution of automation technologies

The evolution of automation technologies is an ongoing process, and it will continue to shape the way we live and work. It's essential for society to embrace these advancements while addressing the associated challenges to harness the full potential of automation in a beneficial and responsible manner.

Here's how one can see the evolution of automation technologies unfolding:

Process Automation: Automation in business processes, known as Robotic Process Automation (RPA), is gaining momentum. It involves the use of software robots to automate routine tasks and workflows

Integration and Convergence: Automation technologies are becoming more integrated and interconnected. They are merging with other emerging technologies like artificial intelligence (AI), and cloud computing to create more sophisticated and comprehensive automation solutions.

AI and Machine Learning: AI and machine learning are at the forefront of automation evolution. These technologies enable systems to learn from data, adapt to changing conditions, and make decisions based on patterns and insights. AI-driven automation is becoming more intelligent and capable, allowing for advanced applications in fields like autonomous vehicles, healthcare, finance, and more.

Can we trust AI as a decision maker?

Even though, one might see the use of AI as a decision engine as a logical next step in the automation, this approach also comes with several pitfalls and challenges that should not be overlooked. Here we can list few:

Lack of Common Sense: Many AI systems lack common sense reasoning, which can lead to nonsensical or irrational decisions in certain situations.

Bias and Fairness: AI models can inherit biases from the data they are trained on, which can result in biased decisions. This can lead to unfair and discriminatory outcomes, especially when the training data is biased. Ensuring fairness in AI decision-making is a significant challenge.

Lack of Transparency: Many AI models, particularly deep learning models, are considered "black boxes" because their decision-making processes are not easily interpretable. This lack of transparency can be problematic, especially in applications where transparency and accountability are essential.

Data Quality and Quantity: AI models heavily depend on the quality and quantity of data. In cases of insufficient or low-quality data, AI may make inaccurate or unreliable decisions. Ensuring high-quality data can be expensive and time-consuming.

Overfitting: AI models can be susceptible to overfitting, where they perform well on the training data but poorly on new, unseen data. Overfit models can lead to incorrect decisions when applied to real-world situations.

Security Concerns: AI decision engines can be vulnerable to attacks and adversarial manipulation. If an attacker can manipulate input data, it may lead to harmful or incorrect decisions.

Legal and Ethical Issues: AI decision engines may raise legal and ethical concerns, especially when they make critical decisions, such as in healthcare, getting a loan or a mortgage, insurance policies or criminal justice. Determining liability and accountability can be challenging.

User Trust and Acceptance: Users may not fully trust or accept AI-driven decisions, especially when they cannot understand the rationale behind them. Building user trust is essential for the successful adoption of AI decision engines.

To mitigate these pitfalls, it's crucial to approach AI decision engines with a clear understanding of their limitations and actively work to address issues like bias, transparency, and data quality.

Therefore, we believe the best way to handle these challenges in finance solutions is by combining traditional, rule-based methods, developed by finance experts over many years, with the latest AI advancements. However, when it comes to making final decisions, we still think it's important to use clear and understandable processes. For example, we use AI-generated scores to assess risks, but any changes in the thresholds used for these decisions should be incorporated into our existing rules and policies.

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