Artificial intelligence businesses need large data repositories to train their AI model on all possible scenarios. Businesses might take illegitimate ways to gather this extensive data repositories leading to negative consequences.
There is a surge in the adoption of artificial intelligence (AI) across many businesses regardless of size, industry, or sector. AI is constantly evolving in decision-making processes and its performance across a wide range of industries. Businesses leverage this technology to understand client needs, optimize service quality, make accurate forecasts, and avoid risks. Designing and Implementing an effective data governance framework is crucial for organizations to make the most out of their gathered data.
Moreover, many industry veterans and regulatory bodies are concerned about the data utilized to train the AI models and how organizations gather it. AI organizations must gather information without compromising the privacy of the user. AI enterprises should have the best practices to gather data while complying with all the rules and regulations led by the regulatory agencies. The recent news of Britain’s Plan to crack down on artificial intelligence companies collecting data got much attention. Thought leaders, AI industry veterans, and other regulatory bodies are exploring opportunities to reduce AI risk and ensure effective data governance to make the most out of the technology.
“Generally speaking, it should not matter that AI companies are scraping the data, as all companies alike should conduct their scraping activities complying with applicable laws. Furthermore, the public availability of data should also not be treated like a universal remedy that would allow scraping companies to forget additional compliance that stems from the data type being scraped. Public data availability is just one of the many parts of compliance to applicable laws,” says Denas Grybauskas, Head of Legal at Oxylabs.
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AI industry veterans should consider data governance one of their top priorities to ensure they comply with all the laws enforced. Effective data governance includes establishing procedures designed to manage and process data efficiently. Designing and enforcing stringent policies guaranteeing an organization’s data’s availability, utilization, integrity, and security is crucial.
In artificial intelligence and machine learning, stringent data governance policies will ensure that all the resources throughout the enterprise always have high-quality data accessible. However, there is a very thin line to how businesses breach compliance laws and regulations. Organizations must be aware of the laws and regulations enforced to ensure they comply with them and avoid legal litigations. Today, due to multiple channels, personal data is available on public domains. Using personal data in the public domain might create trouble for businesses and lead to legal litigations.
“If the data in question is personal data, then privacy laws must be considered. Even if the scraped personal data was publicly available, companies should always research separately for their compliance to-do list. In other words, personal data has additional layers of requirements, even if it is publicly available. Processing of personal data has to have clear legal grounds (consent, legitimate interest, public interest, etc.), and those grounds have to be proved and evaluated before conducting any scraping operation,” adds Denas.
Business leaders need to be very particular about the data type they collect and how they store, utilize and process it. Setting up the best guidelines to govern data is an effective way for organizations to comply with data privacy.
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“The erroneous notion that public data availability somehow cancels out personal data requirements is a major confusion in the industry. These are two separate concepts – a data point can be public and personal simultaneously, which is why establishing best practices and self-regulation on an industry-wide level is so important, as a small misunderstanding can cause great damage to companies and individuals. These reasons, among many others, are why industry leaders are coming together to start the Ethical Web Data Collection Initiative (EWDCI) to establish principles, best practices, and a strong knowledge base for anyone that wants to engage in scraping,” adds Denas.
If AI organizations want to scale should consider data governance as the key to success. AI enterprises must ensure that the information can be consumed by third parties or for developing machine learning models only once it passes quality control checks without significant challenges. Businesses should focus on assessing the data quality gathered to train AI models. AI models need vast data to make accurate predictions or decisions. It is crucial to have effective strategies adopted right from the initial stages of collecting, cleaning, and transforming data.
Moreover, organizations should also have the best data governance strategy to monitor data closely during data integration and model design. The entire pipeline approach gathers and governs data to accelerate the building and debugging of AI models to ensure optimal performance. Organizations focusing on developing AI technologies can consult an expert to define the best data governance policies to achieve success without compromising laws. Gathering valuable data by adhering to all the compliance requirements will yield better results for organizations.
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Nikhil Sonawane is a Tech Journalist with OnDot Media. He has 4+ years of technical expertise in drafting content strategies for Blockchain, Supply Chain Management, Artificial Intelligence, and IoT. His Commitment to ongoing learning and improvement helps him to deliver thought-provoking insights and analysis on complex technologies and tools that are revolutionizing modern enterprises. He brings his eye for editorial detail and keen sense of language skills to every article he writes. If he is not working, he will be found on treks, walking in forests, or swimming in the ocean.
