What Can We Learn From Covid About The Use Of Tech In Policing

What Can We Learn From Covid About The Use Of Tech In Policing? Presented by: Mrs. Kind Date: 6 March 2023 Beyond Face Value: Public Attitudes To Facial Recognition Technology (September 2019) - Survey of 4109 people's attitudes to facial recognition tech - First major survey of this kind on this tech - Looked at 6 used cases: police, transport, supermarkets, airports, job interviews, schools What is the Ada Lovelace Institute doing? - Independent legal review - Citizens' Biometrics Council - Advocating voluntary pause on further deployments The Citizens' Biometrics Council - 60 members of the public - 2 groups: Bristol & Manchester - Meeting across 3 rounds of workshops Digital contact tracing - Design options • Mandatory or voluntary • Infection reporting • Protocol and application • Data collected • Data access • Contact alerting • - - - Actions Technical limitations • Detecting 'contact' • Detecting distance • Vulnerability to fraud and abuse Barriers to effective deployment • Effectiveness needs to be established • Requires accuracy and ubiquity • Requires public trust and confidence • Potentially harmful behavioral impacts Social considerations to be built in • Potential exclusion of vulnerable groups and exacerbation of health inequalities • Direct and indirect social and financial support • Criminality and scams Rapid online public deliberation - 30 members of the public - Evening, virtual workshops, convened with a variety of approaches and styles hour and 1 hour 30 minutes apiece - A total of seven discussions spread out over a three week period; during a fast paced news cycle - Spirit of learning and experimenting - our design challenge - is it possible to adhere to principles of deliberative practice under the constraints of lockdown? What can we learn about what this looks like? Values - Transparency - Proportionality - Protecting the vulnerable - Equality and non discrimination - Safety and assurance - Temporary (changes to freedoms reversible) - Data security and privacy - Personal choice and responsibility - Accessibility Building the evidence base - some key asks - The level of risk the virus presents to society and individuals - Proof that tests and apps are effective - How data from the app will/can be used and fits into wider government, NHS, public health strategy - Evidence base about data security and safeguards - Plus....lessons learned from: • Isle of Wight trial (what happened and what worked/didn't work??) • Global best practice (what happened and what worked/didn't work?) Questions - - - Impact and effectiveness • How do we know it works? • How do we build the evidence base? Responsibilities, equity and fairness • Are we in this together? • What's the likely impact on different groups and people in society? Data rights, security and privacy • Will my privacy be compromised? How far are we willing to trade-off privacy for personal freedom? • How safe and secure is the data? Ideas - Independent review and assessment • Independent Ethics Committee - • Independent intermediary such as data trusts between government and people • Risk assessments and impact assessment • Cost benefit analysis • Independent audit and data deletion process Whole-systems and end-to-end process thinking • Evidence base for effectiveness (and immunity if immunity a core component in ID systems) • Effective approaches to testing, and safeguards against false positives/for reliability - • Communication about how the app itself is working • Humans in the loop i.e. manual contact tracing and support beyond tech • Economic protections for vulnerable people notified of isolation • Mental health and other support for vulnerable people affected by app Clear boundaries and clarity about data rights/responsibilities • Necessity for data use - what's the bare minimum necessary? • Clear, time-limited use of data and process for data deletion • Clarity about purpose and nature of data use and management (long term/short term? Localized or centralized? • Anonymized, pseudonymized, or personal?) • Right to know when you have been categorized or classified within a certain group (and reject that classification) • Right to refuse to download an app or share data (non-mandatory use, with a clear message about responsibilities) • Express provisions and safeguards against data monetization and use of data for commercial benefit