The Ethics of Data Management in the Age of AI

Welcome back to Chronicles of Change, where we explore the opportunities and challenges that emerging technologies present for businesses and their transformation efforts. Today, we delve into the ethical management of people's data, an aspect we touched on briefly in previous issues.

Focus On: How To Manage People Data, Ethically.

As AI-driven technologies become more prevalent, concerns about data collection, usage, and analysis are growing. Companies must navigate the complex landscape of data management, balancing the potential benefits of AI with the ethical considerations of handling personal information.

This is especially true when, after running pilots, data emerges quite quickly as the key ingredient for successful generative AI implementations. Yet, companies must resist the urge to dump all the data they can to fine-tune the model. It's not just a matter of data quality, but also one of ethics and long-term brand equity protection.

To help you chart a path forward, we'll touch on the five Ps of ethical data handling: Provenance, Purpose, Protection, Privacy, and Preparation.

Provenance

Understanding the origin of your data is crucial. Ensure that it was legally acquired and that appropriate consent was obtained. For example, Clearview AI faced backlash for collecting photos without consent and using them for facial recognition purposes. Be transparent about your data collection methods and avoid unethical practices.

Ethical sourcing: Consider the case of a recruitment firm that wanted to test for possible discrimination by hiring companies. The proposed research involved sending thousands of bogus job applications with varying demographic profiles. However, the institutional review board rejected the proposal due to the unethical data collection method. Good intentions are not enough; companies must ensure ethical data collection.

Purpose

When using data, ensure it aligns with the original intent for which it was collected. If you plan to repurpose data for a different objective, consider whether additional consent is required. Don't sell customer data to third parties without proper permission, as this can lead to legal and reputational risks. As such, the purpose must always be traceable so future data users within the organisation can easily understand what they can and cannot do with it.

Dark data: Many companies collect dark data, which is rarely used, often forgotten, and sometimes even unknown. This data has enormous potential value, but companies must ensure that its use aligns with the original purpose and ethical considerations.

Protection

Safeguarding data is a top priority. Implement robust security measures to prevent unauthorized access, data breaches, and potential misuse. Regularly review and update your security protocols to stay ahead of emerging threats.

Cost of protection: Data breaches can be costly. For example, JPMorgan Chase had to spend $250 million annually on data protection after a breach compromised 76 million people and 7 million businesses. Investing in data security is essential to protect your company and customers.

Privacy

Respect the privacy of individuals by ensuring that their personal information is handled with care. Comply with data protection regulations like GDPR and be transparent about how you collect, use, and store personal data. Develop privacy policies that communicate your practices to customers and stakeholders.

Anonymization techniques: Striking the right balance between too little and too much anonymization is crucial. Techniques range from aggregating data to pseudonymizing it with random, nonrepeating values. However, researchers have been able to identify people in a dataset using minimal information, so companies must be vigilant in protecting privacy.

Preparation

Prepare your data for use by cleaning, organizing, and ensuring its accuracy. This helps to minimize errors and biases in AI-driven decisions. Invest in data management tools and processes that enable you to effectively leverage your data while maintaining ethical standards.

Data stewardship: Recording the ethical provenance of data and automating data stewardship can help ensure that data is managed ethically throughout its lifecycle. Companies should train personnel to maintain records of ethical provenance and invest in tools that support data stewardship.

In the coming issues we will delve in each of these categories, unearthing powerful technologies and processes that help maximise the value of data in an ethical and brand-safe way.

Spotlight on: Synthesia

Synthesia leads the AI video generation field (long before genAI was coined as a term). Through their user-friendly online platform, you can generate videos featuring human presenters by inputting text. There's a selection of over 125 AI avatars, modeled after real actors, capable of speaking in 120+ languages. Definitely worth a try!

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