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Data and Privacy: How to Balance Sharing and Protection in the Big Data and AI Era

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Picture yourself as a patient who needs a life-saving treatment that is not available in your country. You have the option to join a clinical trial that involves sharing your personal health data with researchers from different countries and organisations. Would you agree to participate? What if you knew that your data could be used for other purposes, such as developing new products, services, or policies, without your consent or knowledge? How would you feel about the potential benefits and risks of data sharing in this scenario?

Data sharing is the process of making data available to others for various purposes, such as research, innovation, education, or public service. Data sharing can have many benefits, such as enhancing scientific discovery, improving social welfare, increasing economic growth, and fostering collaboration. However, data sharing can also pose many risks, such as violating privacy, enabling discrimination, facilitating exploitation, and undermining trust. Therefore, data sharing requires careful consideration of the ethical, legal, and social implications of how data is collected, stored, accessed, used, and reused.

The Benefits of Data Sharing in the Era of Big Data and AI

Big data refers to the large volume, variety, and velocity of data that is generated from various sources, such as sensors, devices, platforms, or networks. AI refers to the use of algorithms and systems that can perform tasks that normally require human intelligence, such as learning, reasoning, or decision-making. Big data and AI can enable new forms of data analysis and applications that can generate valuable insights and solutions for various problems and challenges.

For example, according to a World Health Organisation (WHO) report, data sharing can enhance scientific discovery by enabling faster and more efficient research collaboration across disciplines, sectors, and regions. As an example of data sharing for innovation, scientists in South Korea’s Korea Superconducting Tokamak Advanced Research (KSTAR) facility have managed to sustain a nuclear fusion reaction running at temperatures in excess of 100 million°C for 30 seconds for the first time.

This breakthrough could pave the way for clean and unlimited energy sources in the future. As an example of data sharing for education, Khan Academy is a non-profit organisation that provides free online courses and resources for learners of all ages. By using big data and AI to personalise learning experiences and track progress, Khan Academy has reached over 100 million students worldwide. As an example of data sharing for public service, the United Nations (UN) has launched a platform called UN Global Pulse that uses big data and AI to monitor and respond to global issues such as poverty, health, climate change, and human rights. By using real-time data from sources such as social media, mobile phones, satellites, or sensors, UN Global Pulse can provide timely and actionable insights for decision-makers and stakeholders.

The Risks of Data Sharing in the Era of Big Data and AI

However, big data and AI can also create new challenges and threats for data sharing, such as increasing the scale and scope of data collection and use, reducing the transparency and accountability of data processing and outcomes, and amplifying the potential harms and impacts of data misuse and abuse.

For example, according to a report by the International Data Corporation (IDC), the amount of data created and consumed in the world will grow from 59 zettabytes in 2020 to 175 zettabytes in 2025. This means that more and more data will be collected and used by various actors for various purposes, some of which may not be aligned with the interests or preferences of the data subjects. Moreover, the complexity and opacity of big data and AI systems may make it difficult or impossible for the data subjects to understand or control how their data is processed or what outcomes are produced. Furthermore, the misuse or abuse of big data and AI systems may result in serious harms or impacts for the data subjects, such as identity theft, financial loss, psychological distress, social discrimination, or physical harm.

For example, according to a report by the Electronic Frontier Foundation (EFF), there have been many cases of privacy breaches, data leaks, or cyberattacks that have exposed or compromised the personal data of millions of people. As an example of a privacy breach, Facebook has faced several scandals involving the unauthorised or inappropriate access or use of its users’ data, such as the Cambridge Analytica case involving data manipulation for political purposes. As an example of a data leak, Equifax, a credit reporting agency, has suffered a massive data breach that affected the personal and financial information of 147 million Americans. As an example of a cyberattack, the Colonial Pipeline, a major fuel supplier in the US, has been hacked by a ransomware group that demanded a payment of $4.4 million to restore its operations.

The Elements of Balance for Data Sharing in the Era of Big Data and AI

Therefore, it is important to balance the benefits and risks of data sharing in the era of big data and AI. This requires a holistic and multidisciplinary approach that involves various stakeholders, such as data providers, users, regulators, intermediaries, and beneficiaries. Some of the key elements of this approach are:

1. Establishing clear and consistent rules and standards for data sharing that respect the rights and interests of all parties involved. This includes defining the purpose, scope, conditions, and limitations of data sharing; ensuring the quality, security, and integrity of data; protecting the privacy and confidentiality of data subjects; preventing the unauthorised or inappropriate access or use of data; and enforcing the compliance and accountability of data actors.

2. Promoting fair and equitable data-sharing practices and outcomes that balance the costs and benefits of all parties involved. This includes ensuring the consent and participation of data subjects; providing incentives and rewards for data providers; supporting the access and availability of data for users; enhancing the value and utility of data for beneficiaries; and addressing the potential biases or inequalities in data distribution or representation.

3. Fostering a culture of trust and responsibility for data sharing that encourages collaboration and communication among all parties involved. This includes raising awareness and education about the opportunities and challenges of data sharing; engaging in dialogue and consultation with relevant stakeholders; building capacity and competence for data literacy and skills; developing mechanisms for feedback and evaluation; and creating platforms for innovation and learning.

Data sharing is not a simple or straightforward process. It involves multiple dimensions, factors, and trade-offs that need to be carefully weighed and balanced. Data sharing can have significant benefits for society but also pose serious risks for individuals. Therefore, we need to be mindful of how we share our data in the era of big data and AI. We need to be informed about our rights and responsibilities as data subjects. We need to be involved in shaping the rules and standards for data sharing as data providers. We need to be vigilant about the practices and outcomes of data sharing as data users. And we need to be proactive in creating value from our shared data as beneficiaries.

Data sharing is not only a technological challenge, but also an ethical one. It requires us to rethink how we collect, store, access, use, and reuse data in ways that respect the rights and interests of all parties involved, promote fair and equitable practices and outcomes, and foster trust and collaboration among data actors. How can we achieve this balance? What are the best practices or frameworks that we can adopt or develop? How can we ensure that data sharing serves the common good rather than private interests? These are some of the questions that we need to address as we navigate the nexus of data sharing and privacy in the era of big data and AI.

By Oluwole Asalu

Oluwole Asalu, champions the fusion of data sharing and privacy in the age of Big Data and AI, writes from Lagos, Nigeria,

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