On Friday afternoon the C40 committee was joined by Dr. Astrini Sie – machine learning research engineer for project archer — to discuss the topic of ‘data collection for law enforcement’. Data is information about the individuals which characterises or identifies them. Data collection dates back to the 1800s when officials used paper documents to verify slaves’ permission to go out in public. It can be categorised in visual information, auditory information, physiological information, social data and lifestyle data. When applied as a whole, such information constructs an accurate image of the individual.
In terms of governmental data collection, CCTV cameras are one of the most used devices to acquire information in public spaces. Yet, more importantly — in terms of law enforcement — body cameras are used by police to collect information and, subsequently, construct a database used to identify suspects. Data collection can work towards society as a whle. For example, Covid-19 long-term tracking apps use intricate databases which solely rely on public data collection. Yet, Dr. Sie refers to data as money; profitable, yet dangerous, as seen in the 2016 election scandal in which thousands of facebook user’s data was sold illegitimately. Additionally, governments sometimes use data collection and acquisition to establish authoritarian regimes, which, in turn, are entirely monitored and controlled by data acquisition methods such as CCTV.
Upon such statements, delegates asked Dr. Sie what organisation can do to minimise the ‘bad’ in data collection. To this she replied that companies have to de-identify the individual — by blurring out faces and not recording voices — and construct algorithms which are unbiased by making sure that the same amount of data is acquired from the majority and minority groups. Dr. Sie concluded her talk by presenting a series of questions to the audience: What can we do to maximize the good? How will the methods by which data is collected and processed change in the future? How is data collected ethically ?
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