Ethical AI (1 Blogs)

What Is AI Ethics and How to Implement It ?

Published on Jun 12,2024 28 Views

Prateek
Enthusiastic Computer Science fresher, skilled in development, data analysis and effective communication... Enthusiastic Computer Science fresher, skilled in development, data analysis and effective communication with a strong passion for continuous learning.

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In today’s ever-evolving digital era, we have to agree that AI has become an integral part of our daily lives. For example, You wake up and ask your virtual assistant to schedule your day. You get in your car, and the navigation app suggests the best route based on real-time traffic. At work, you use AI tools like ChatGPT, Claude, or Google Bard to help with research, writing, and analysis.

AI is truly embedded in so many aspects of our modern existence. But have you ever stopped to think about the impact AI is having, and by impact, I mean that directly or indirectly, AI is making decisions that will shape your day-to-day life. Imagine going for a job interview, and an AI system scores your resume, determining whether you get shortlisted or not. In healthcare, AI is increasingly being used for diagnosis and treatment planning. In finance, AI algorithms analyze data and make trading decisions. In criminal justice, AI tools are used for predictive policing and risk assessment.

But will AI remain accurate and unbiased while making all these decisions?

This question is what AI Ethics all about is, and in this blog, we will discuss the answer to this question and understand what AI ethics is exactly.

 

What Is AI Ethics?

AI ethics refers to the principles and practices that ensure artificial intelligence systems are developed and used in a way that is ethical, unbiased, transparent, and accountable.

Ai ethics

Now, AI Ethics is a subset of Responsible AI that refers to the principles and practices around developing and deploying artificial intelligence in a way that is ethical and trustworthy and benefits humanity as a whole.

Today, AI systems seem to be unbiased, but if not designed carefully, they can continue and amplify societal biases.

The best example of this is Google Gemini.

Gemini logo

Gemini made some really bad mistakes in the past. It showed pictures of German soldiers from World War 2 as people of different races, which is not accurate. It also made up stories about historical figures like the Founding Fathers of America and the Pope, showing them as different ethnicities than they really were.

Even worse, Gemini said something crazy – that Elon Musk posting memes online was more harmful than Hitler! In India, it falsely claimed that PM Modi enforced fascist policies, which is not true at all. However, when asked about other world leaders like the President of Ukraine, their responses were not as controversial.

These big mix-ups by an esteemed platform like Gemini show why “AI Ethics” is so important.

Now, the question comes of how can we build Trustworthy AI Models. Let’s understand that in the next section

 

How can we build Trustworthy AI Models?

In order to Build Trustworthy AI models, there are five pillars to keep in mind:

  1. Fairness
  2. Explainability
  3. Robustness
  4. Transparency
  5. Privacy Protection

Ai Model

Now, let’s understand them one by one.

Fairness:

Fairness is basically ensuring that AI systems treat everyone fairly and do not discriminate against underrepresented or disadvantaged groups. 

For example, you can take an AI-powered recruitment system that ranks job candidates without discrimination on the grounds of age, gender, ethnicity, or other protected traits   by using carefully curated training data and bias testing.

Explainability:

It means being able to understand and explain how the AI arrived at its decisions or outputs based on the training data and machine learning methods used.

For example, a loan application AI not only outputs a decision but can also show the key factors, such as income, credit score, debt, etc., that drove its approval/denial recommendation.

Robustness,

AI systems need to be secure, reliable, and safe – preventing them from being hacked, manipulated, or behaving in unintended ways that could harm individuals.

Example: A self-driving car AI with built-in safety constraints and backups to prevent dangerous behavior if the system malfunctions or is hacked.

Transparency

The use of AI to drive decisions that impact people should be proactively disclosed and details provided about the AI’s purpose, training, and functionality.

Example: A healthcare provider disclosing upfront that an AI system helps analyze medical scans and provides information about its capabilities and limitations.

Privacy Protection

Any personal data used to train AI models must have robust data governance, de-identification, and protection of individual privacy.

Example: An AI virtual assistant that does not store or use personal conversation data for training unless users explicitly opt-in to share their data.

If AI systems don’t follow these rules, they might keep unfairness going, make decisions without anyone checking if they’re right, accidentally hurt people, invade privacy, and make people lose faith in them. The examples show why it’s important to use AI in a fair and responsible way that helps everyone.

 

How do we establish Ethical Boundaries?

Now, after checking the pillars, how do enterprises determine if their solution is at risk of crossing some sort of ethical boundaries?

Let’s check that out!

Imagine you work for a large e-commerce company which makes product recommendations to users based on their browsing   and purchase history. While this AI system is designed to enhance the customer experience and increase sales, there are potential ethical concerns that need to be addressed.

As the AI ethics lead at your company, you realize that it’s crucial to establish clear guidelines and principles to ensure your AI solution operates within ethical boundaries. You propose the following three core principles:

  1. AI should augment human intelligence, not replace it: The AI recommendations should assist customers in making informed decisions, not manipulate or deceive them.
  2. Data and insights belong to their creators: Customer data should be used transparently and with consent, respecting privacy and ownership.
  3. Solutions must be transparent and explainable: There should be visibility into how the AI system is trained, what data is used, and how it generates recommendations.

To ensure these principles are followed, you suggest implementing a mapping exercise. This involves listing all the features and intended benefits of the AI recommendation system and then identifying potential negative consequences or ethical risks associated with each feature.

For example, while the AI system’s ability to personalize recommendations based on browsing history can enhance the customer experience, it could also lead to privacy concerns or promote biases if the training data is not diverse enough.

After mapping out the benefits and risks, you work with your team to implement specific rules the AI system must follow. These might include rules such as:

1)Customer data will not be sold to third-party advertisers.

2)The AI system will be trained on a diverse and inclusive dataset to avoid biases.

3)Customers will have the option to opt out of personalized recommendations or access explanations for why certain products are recommended.

To further mitigate ethical risks, you leverage open-source tools like Interpret ML or AI Fairness 360, which helps detect and mitigate bias in machine learning models. You also explore tools to ensure compliance with privacy regulations and to measure the uncertainty or confidence levels of the AI system’s recommendations.

Through this proactive approach, involving clear principles, risk assessment, rules, and monitoring tools, your e-commerce company can build trust with customers and stakeholders, ensuring that the AI solution enhances the customer experience while operating within ethical boundaries.

 

Conclusion

According to Exploding Topics, around 77% of companies are either using or exploring AI, which evidently shows that ethics in AI impact not only individuals but even organizations that are massively dependent on AI Tools. Now imagine what could be the consequences if the AI model that the companies are using lacks ethical configurations. The whole industry will be hugely impacted, and this creates the importance of learning AI Ethics.

Now, If you want to learn more about “What is AI Ethics?” then enroll in our ‘Ethical Artificial Intelligence (AI) Certification Course.

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