Ethical Data – Why Your AI Models Needs It

Subtitle: Responsible Data Collection and Use is the Foundation of Ethical AI

An Intro to Ethics

The terms, “ethical data” and “responsible data” can be ambiguous in meaning. In the world of tech and AI data, ethics refers to responsible gathering and use of data to train models as well as ensuring that those models interact with humans without bias.  It’s not only important to gather and use the data responsibly for the model, but the model itself needs to have a positive impact on society and not be used for unethical gains.

According to our 2022 State of AI and Machine Learning report, 93% of respondents agree that responsible AI is a foundation for all AI projects within their organization. As an integral component for all AI projects, ethics is the fifth and final takeaway of the report. We’re seeing an increased focus on making sure all stages of the AI lifecycle are conducted responsibly, with the main focuses on bias reduction and ethical data sourcing.

The Basics of Ethics

To make sure your machine learning model is created with ethical and responsible AI, it needs to encompass the following:

  • Free of personally identifiable information (PII)
  • Receive permission to collect data from each contributor
  • Contains data from humans representing all demographics
  • The outcome will help and not hinder the greater good
  • The person(s) collecting the data remains neutral throughout the process
  • Adhere to state and government data compliances

 

The requirements seem simple but diligence across all data sourcing, preparation and evaluation efforts takes a true dedication to delivering ethical AI products.

Responsible Data Collection

One of the biggest pushes to ensure data is ethical and sourced responsibly is the result of initial shortcomings in data collection. There’s a misconception that AI models can be trained correctly using small volumes of data. If there’s not enough data to properly train a model, the limited dataset could potentially introduce bias into the model. An example is an AI powered pet app that suggests food or toys. If the data is based on people who only own cats, the model won’t be trained to provide proper suggestions for dog or bird owners.

One of the best methods to ensure data is ethically sourced and unbiased is to employ someone who isn’t emotionally invested in the project to gather the data. Even with the best intentions, it’s easy for anyone to unknowingly gather more data that skews towards ideas they prefer or already align with. In the case of the pet app example, someone who prefers dogs could unintentionally gather more data from dog owners than other pet owners. In the result is a bias that would make the app work best for dog related queries than other pets. Enlisting a neutral third-party to gather data and putting the proper safeguards in place to ensure data gathered is from a diverse representative set of contributors, those engaging with your model will benefit equally.

Ethical Data Preparation

After all the data is gathered, PII must be removed to ensure the privacy of contributors is maintained. This is especially important for healthcare-related machine learning models because sharing a patient’s health status is a violation of HIPPA. One way companies prevent PII from becoming an issue is through the use of synthetic data. These datasets will always be free from PII and it can help generate data for less common use cases, covering all scenarios for your model. Another way to prevent PII is working with Quadrant and our Geolancer program that automatically removes PII from uploaded point-of-interest and image datasets before it’s given to customers. To ensure the data we used is gathered ethically, we utilize our global crowd to collect data across all demographics, which prevents bias from appearing in the model.

Ethical AI in Action

Though many AI models are developed with the intention of improving lives or simplifying a task, good technology in the wrong hands can have dangerous consequences. Companies creating AI projects have to consider the real-world use of their finished product.

For most programs and products, the benefit of people using it is clear. It can be simple like getting recommendations on items to buy or using a program to edit a paper for proper grammar usage. However, the data used in the model comes from people’s life and there are people in the world who will try and reverse engineer the data. They do this in hopes of discovering people’s identity or alternatively modifying an existing program to be used for unethical gains. This is why governments around the world have created special data requirements to ensure all data is obtained ethically and responsibly so that it can’t fall into the wrong hands.

If all the above steps are taken to ensure the data is gathered and used responsibly, then the model will be trained ethically. The final product will work as intended and have a positive impact on every consumer’s life. With all the potential risks and benefits it’s clear to see why business leaders and technologists in our survey are in agreement of its importance.

Learn More about Ethical Data

Ethical data is critical to AI model success and industry experts share their thoughts in our 8th annual State of AI and Machine Learning Report that you can read today to better understand the current industry trends and challenges in relation to data ethics, as well as read our other four key takeaways. For further information, watch our on-demand webinar, where we discussed in-depth all topics covered in our State of AI Report.

Want to learn more about ethics? Check out these articles in our Ethical Data series –

Ethics at Every Stage of the AI Lifecycle: Data Preparation

Ethics at Every Stage of the AI Lifecycle: Sourcing

 

 

 

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