Ethics issues across data life cycle
1. Ethics issues across data life cycle
Hello! Join me to explore ethics across the data life cycle!2. The data life cycle
Data ethics cover every step of the data life cycle within an organization. Data goes through different stages- starting with data acquisition, which involves data collection or sourcing for a particular project or business need. The next stage is data preparation which is cleaning, labeling, and quality control of the acquired data— following is the data storage stage, which deals with the infrastructure set-up to handle the data volumes securely. Data is then analyzed using various methods and technologies, including AI, to discover insights that aid data-driven decision-making in companies. And, finally, data retention, archival, and data sharing. Let's examine the data ethics-critical stages, starting with ethics in data acquisition.3. Data acquisition
There are many ways to acquire data- through forms or surveys, sensors, wearables, mobile phone apps, and buying it from third-party suppliers. Before beginning data collection, companies should consider whether they are allowed to collect data in the first place- in terms of respecting privacy and copyright laws. They also need to set a clear goal for data collection rather than amassing a lot of data because it's nice to have a lot of data. Data acquisition needs to be purposeful, efficient, and less intrusive. You should also consider if you are collecting representative data consisting of all demographic groups. Be respectful of people's time and effort, and compensate them if necessary. Most importantly, you need to establish that individuals give their explicit, informed consent for the intended use of data. And, of course, it's important to vet your data suppliers to check if they have the same ethical standards. Moving on, we'll discuss ethical issues in data preparation.4. Data preparation
Data must be enriched to prepare for big data analytics and AI projects. The data preparation steps involve data cleaning, annotating, and labeling. That includes transcribing audio files to text, labeling parts of an image, and flagging inappropriate content. Human annotators often do this work. Many companies outsource data preparation to lower-income countries, where human annotators have poor working conditions and are not properly trained. For instance, TIME magazine uncovered exploitative working conditions of data enrichment workers in Kenya to detect hate speech in the training data used for ChatGPT. In addition to the obvious ethical red flags, poor training may lead to inconsistent data quality and biased datasets due to subjective labeling. Now let's look into the data storage considerations for data ethics.5. Data storage
When you store data within your organizational infrastructure, you have the ethical and even legal obligation to protect the confidentiality and integrity of the data you keep and prevent data breaches or accidental loss. You also need to ensure that your data is secure and put in measures to protect against unauthorized access. These measures can be technical: infrastructure choices, methods, and techniques for secure data storage. Or organizational- like data protection policies and training programs. And finally, let's see what data ethics says about data sharing.6. Data sharing
Many companies have to share their data externally to collaborate with others and to build innovative products. Some companies also monetize their data. Data sharing can be a positive data ethics outcome if done responsibly. For instance, the accelerated data sharing among researchers and companies during the COVID-19 outbreak allowed scientists to trace and monitor the COVID-19 pandemic faster than any previous outbreak, saving millions of lives. However, to share data, an organization should have a clear overview of data ownership, document the lineage of data provenance, and individuals' consent for eventual data reuse or sharing. They must also respect all data privacy, and IP regulations, and individual rights while sharing data. Whenever possible, they should use privacy-enhancing technologies to protect the data while sharing it. Overall, at each stage data ethics principles will enhance data life cycles.7. Let's practice!
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