Navigating AI ethics: Key considerations in machine learning development

AI & Customer Intelligence

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AI is a core function of many business processes. Some are lighter touch, such as chatbots on sales pages answering simple questions, while others have more significant implications, like AI in banking risk management. While there are numerous benefits to AI, there’s an underlying shadow to increased automation and efficiency. Picture a world in which AI programs grow without restrictions, or are put in charge of governance and public service, yet each has a tinge of bias in its underlying code. These seemingly harmless biases can translate into real-world discrimination and harm.

Now, what if how AI makes decisions is a total mystery, with no one taking responsibility? Without clear rules for AI, it’s like handing over the steering wheel without knowing where the car is going. So, here’s the big question: will we let AI run wild, or should we put some fences in place so it doesn’t drive us off a cliff?

The ethics question for AI

When discussing AI ethics, we need to examine it under several critical criteria:


Transparency emphasizes the need for clarity in how AI systems operate. Without transparent processes, users are left in the dark about decision-making, creating a breeding ground for suspicion and mistrust.


Accountability gives us a clear line of responsibility for any actions the AI takes or doesn’t take. Ignoring this principle can result in a vacuum of responsibility, where errors or biased outcomes lack clear attribution, hindering efforts to rectify and learn from mistakes.


In the Western world, we consider privacy a valued right. Ethical considerations for AI need to consider individual privacy to avoid AI engines collecting private user data without consent. The erosion of privacy compromises personal freedoms and undermines trust—essential for the widespread acceptance of AI technologies.


Ever since the early days of AI in facial recognition software, its lack of fairness in categorizing faces of different geographic locations has been an issue that has exacerbated societal divides. Ignoring fairness can lead to models that are technically correct, but that may impact a sector of society negatively or even actively discriminate against them.

These core principles can serve as a map to help AI find the correct route to its endpoint while avoiding any pitfalls it might encounter on the way there. When implementing AI, these principles should serve as guidelines for what we, as humans, should and shouldn’t do.

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The challenges of implementing ethics in AI

Defining the elements of AI ethics is one thing, but implementing those factors in an AI model is something entirely different. In recent years, many researchers in the field have realized how difficult it is to implement these ethical boundaries properly. Some of these challenges include:

Training data bias

AI’s conclusions are built on iterative methods trained on a particular data set. If the set misrepresents the population, its results will have that bias built in. For instance, if medical AI tools include only a small minority population in their data set, they might underrepresent or misinterpret that group’s needs due to their limited statistical significance. In fact, algorithmic methods have been proven to have a racial bias in the recent past

AI’s “black box” model

Unlike typical algorithms with a series of steps that produce a predictable result, AI is more amorphous in how it does what it does. Researchers often refer to AI processing as a “black box” since they don’t see how AI makes its decisions directly. This lack of transparency can make it challenging to streamline models, not to mention making it more opaque and less trustworthy for non-specialists to grasp. However, being fully transparent has its issues since it could make AI more susceptible to hacks or even be reverse-engineered and reconstructed to remove competitive advantage. 

No standardization and regulation in the AI space

AI is like the Wild West of tech at this moment because many developments are occurring without comprehensive regulation. Different organizations may interpret ethical guidelines in their own way so that it doesn’t jeopardize their development. While the White House has issued guidance on managing the risks of AI, the most significant regulation and standardization issue is balancing the public-facing side of AI while making the industry safe for innovation. That is, ensuring that companies can innovate without damaging or compromising their users or customers.

Global and cultural variations

Culture varies from place to place. Defining a global AI ethical framework would require taking each of those cultural variations into account. AI developed in the Western world adopts many of its attitudes towards data security and personal rights. However, a similar application designed for a country like China would have a different idea of what rights and freedoms users should have. Finding a middle ground on these is crucial for successful AI development, but compromises can be made in the interest of a global framework.

Solutions for implementing AI ethics

Ethical AI implementation is a complex topic with multiple solutions; however, here are a few to consider when building AI models. 

Using diverse and representative data

Biased data will always give biased results. It’s just another example of the garbage-in-garbage-out cycle. Therefore, unbiased data must come from multiple areas, cultures, and nationalities, encompassing a broad scope to truly capture the human element. 

Interpretable and explainable AI (XAI)

AI doesn’t have to exist within an inexplicable region. Researchers are already working on explainable AI (XAI) models that help them “see” into the black box of AI thinking. Through false positive and false negative detection, they can explain more of the AI’s thinking process, making it more transparent to the average individual.

International collaboration in AI ethics

Aside from the US, several other countries are interested in pursuing ethics in AI development and implementation. A global AI summit gives us some hope for a unified ethical framework, but it will be a long road to get there. For now, each country is striving to develop its own AI framework that governs development in its jurisdiction. 

Development of cultural sensitivity

The internet has made the world a smaller place, but even so, it has outlined many inherent divisions we have as humans. Cultural sensitivity comes through communication. Day by day, we meet and interact with more people on the global stage, helping us grasp cultural nuances and learn from those differences. With AI-driven customer management tools, cultural sensitivity is an achievable goal in the future, allowing marketing departments to have a better grasp of the cultural and social issues affecting their customers.

Ethical AI in customer feedback analytics

Idiomatic embraces this idea, building AI models that are useful, transparent, and actionable in their recommendations.

Businesses rely on unbiased, clear, actionable insights to improve their products and services. Idiomatic’s AI implements the ethical handling of customer feedback while ensuring consumer data security. Idiomatic offers AI-driven customer intelligence, empowering organizations to implement changes that resonate with customer needs and preferences. Idiomatic aims to minimize biases and enhance decision-making transparency. Ethical considerations include:

Ethical data collection

Customer feedback data collection and processing is one of the pillars of Idiomatic’s AI model. All feedback data is sourced ethically and transparently, and businesses can inform their customers as to what level their feedback data is being used.

Clear interpretation

Understanding the intricate nuances of customer feedback and language is pivotal. Idiomatic’s AI model is designed for interpretability, analyzing all the interactions customers have with your brand from all data sources (including app reviews, helpdesk tickets, chatbots, surveys, forums, and more). Idiomatic’s contextual machine learning allows a company to build a better customer intelligence strategy by classifying data based on the language of particular parties without making judgment calls or decisions for customers. This empowers businesses to comprehend the basis for feedback, facilitating targeted improvements and strategic decision-making. For example, Idiomatic’s human-supervised models cannot invent predictor variables so are prevented from using protected pieces of information such as race, gender, sexual orientation, etc. to do classification.

Consistent review

As noted before, models are only as unbiased as the data used to train them. Idiomatic’s system constantly reviews and assesses training data to provide the best possible input for the model. This leads to reduced downside risk and externalities by ensuring that any AI system onboarded has been thoroughly vetted for ethical flaws.

The future of ethics in AI

With global discussion about AI reaching a fever pitch, two things are evident in each instance: Governments want to have a say in how AI works and what it can do, and AI has a responsibility to humanity in what it provides and what it collects. Responsible AI companies already understand these factors and have spent a long time thinking about them. In the case of these companies, the safety rails to prevent their AI cars from running off the cliff were already established long ago.

Start using the power of customer feedback to make impactful business improvements.  Learn how Idiomatic’s contextual machine learning can help your business gather and process customer feedback data without biases and with clear, actionable interpretation. Schedule a free, no-obligation demo with Idiomatic to learn more!

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