Data for London

This report was produced for the Greater London Authority, looking at digitalisation and the opportunities it presents for the London Office of Technology and Innovation.

5th April 2018

1. Digitalisation and Trust

The rapid development of innovative technologies in cities around the world demands that the GLA, on behalf of London’s citizens, supports innovation projects to improve the delivery of public services on par with other global cities. The newly-created London Office of Technology and Innovation (LOTI) aims to champion technology preparedness and explore new uses of data and digital technology in order to drive efficiency and improve the quality of public services. However, in a climate of public mistrust of data collection, a central challenge needs to be addressed: ​How can London’s citizens trust that their data will be used fairly in innovative projects?

This report seeks to explore this challenge from the perspectives of the disciplines of business studies, psychology and social justice, before offering insights and experimental solutions that would support future innovation projects in the capital while engaging London’s citizens in the process of shaping their city and their future. The combination of disciplines illuminates the challenge in several ways. Psychology provides several frameworks for considering the foremost issue of trust; business studies highlights the need to prioritise economic efficiency, while the priorities of social justice stand in opposition to those of business and instead champions the just distribution of risk and benefit among London’s citizens. Arguably, the challenge of ensuring that London’s citizens trust that their data will be used fairly in innovative projects is a ‘wicked problem’, where there is no perfect solution that will satisfy all parties. However, due consideration of the complexity of the challenge can lead to greater success.

Against a backdrop of growing resource pressures in areas like energy, water management, transport, healthcare and housing, London is faced with numerous challenges and constraints that continue to strain its infrastructure. However, London can benefit from the unique opportunity presented by digital innovation in the form of new technologies that can help local authorities meet public challenges. These range from using internet-based live sensors to monitor air pollution to closing information gaps about local revenue collection via distributed ledgers like blockchain.

London

Without private investment in new technologies and the ability to ensure the existence of spaces for testing their efficiency, Londoners are unlikely to receive maximum benefit from public services. Furthermore, given the uncertain political environment regarding the retention of businesses and skilled workers post-Brexit, the GLA can help ensure London will retain or even boost its status as a leading urban centre through technology preparedness.

This critical challenge spills across many different disciplines and involves multiple stakeholders at several levels beyond just citizens and public service providers. With different levels of power and interest, competition amongst stakeholders may also be enhanced and some institutions end up retaining an unfair informational advantage over others. For example, competition for private investment between different boroughs collecting data from citizens may lead to an unwillingness on the part of a financially stronger borough to share with a weaker one. The 33 London boroughs also must work with other entities including Thames Water, Ofgem, National Grid, TfL, London Councils and the GLA. These entities have different responsibilities but overlapping interests. The current lack of information sharing results in both limited capacities on the part of each stakeholder and coordination gaps.

The role of data is thus central to bridging some of the challenges that London faces with respect to resource constraints, technology preparedness and internal collaboration, and yet the role of data in public services faces complexities in itself.

Data

Data can be seen as a good which can be brokered, bought and sold by corporate entities. This is common in the US market as well as other markets around the world. Additionally vendors (e.g. telecommunication providers) may directly collect data from their consumers, and feed this data into products and services that ultimately benefit those consumers.

However, in the last few years, the role of the consumer as a passive, rational user of data has shifted into the realm of controversy. Challenges for data use and management pertain to the ability of both private and public bodies to exploit personal data in a way that shapes consumer preferences. These contentions have been exacerbated by a series of events, such as AOL’s search leak in 2006 that exposed 650,000 users’ private data, to the WannaCry ransomware attack that caused large scale disruption in public services including the NHS. The recent scandal of Cambridge Analytica’s use of over 30 million Facebook profiles to allegedly influence the US presidential election led to a nearly 6% drop in Facebook’s stock price, uproar about individual rights, and magnified the scale at which data-misuse can shake public trust (BBC, 2017; Shen, 2016).

The implementation of the General Data Protection Regulation (GDPR) in Europe in May 2018 attempts to bolster trust in public bodies and safeguard individual rights, such as the right to be informed, to erase and to refuse (ICO, 2017). Additionally, as more data is collected by IoT sensors and public WiFi services, the issue of consent when that data is re-used becomes a serious challenge (see Figure 2). In the case of public WiFi, TfL’s collection of data on devices using their WiFi network provides an opportunity for earnings of £322m over a period of 8 years, by providing more accurate information to advertising companies, but risks jeapordising individual rights (Cheshire, 2017).

In light of the some of the events mentioned above, the GDPR is also a very strong indicator of the need to enhance public trust in bodies that retain, collect, or use data for public services for such to be effective even if the re-use of data without consent is aimed at public rather than private benefit. To highlight some of the changes that set the context around data collection and management in London, the following table highlights some of the key changes between the existing Data Protection Act (DPA) and the GDPR.

Challenge

LOTI can bridge the gap between the opportunity of technology preparedness and the challenges facing London, through enhancing its position as a virtual organisation. Apart from its ability to retain independence from private bodies, in which consumers have considerably low trust (Royal Statistical Society, 2014), LOTI is also at arm's length from public bodies such as boroughs, which enables it to achieve the overarching aim of enhanced collaboration without being perceived as preferring particular groups of citizens.

By ensuring data is available, present in large enough quantities for processing and of high quality, LOTI is in a unique position to increase ​meaningful insights for private and public bodies while ensuring that ​measurable public value ​is being created for private citizens. This puts LOTI in a position that will enable it to feed into the following:

• Anticipation of innovative technologies for London to be prepared to deliver efficient and effective services for citizens.

• Capacity building around existing services in order to accommodate new business models for these deliveries.

• Responsiveness to new learnings via lean testing methods that will prevent London from falling behind change.

Therefore, in line with its position between the public and private sector, and its overarching objectives concerning citizen engagement and fairness, this report focuses on ​how LOTI can ensure that London’s citizens trust that their data will be used fairly in innovative projects​.

For purposes of clarity, London’s citizens are considered people who live, work or pass through London and hence use public services that collect data. The concepts of trust and fairness are central to individual rights and the context surrounding data as mentioned above, and will be further explained from the perspectives of literature from social justice and psychology. Particularly, concerns like protection from risk, trust between entities (e.g. boroughs) and individual perceptions about privacy and security will be explored. Finally, innovative projects are those which fall into LOTI’s overall aims concerning the facilitation of technological preparedness, private-public partnerships and new businesses in London.

The following sections serve to expand on this challenge by providing an in-depth analysis of both the stakeholders and the opportunities as well as limitations that informed the selection of the aforementioned challenge.

Analysis

The below SWOT analysis outlines some of the beneficial and challenging factors for LOTI and the wider environment of stakeholders in the challenge.

Stakeholder Analysis

In respect of this challenge, LOTI exists in a complex environment of stakeholders which have both an interest in the issues at the heart of the challenge and the power to influence the success of the experiments we propose. The diagram below aims to position the stakeholders according to their level of interest and power.

2. Three Perspectives

Three disciplines contribute a new perspective on the challenge of how London’s citizens can trust that their data will be used fairly in innovation projects: psychology offers insights into the idea of trust and how best to encourage trust; social justice asks questions to ensure that the risks and benefits of innovation are justly distributed to all citizens; and business studies provides a valuable consideration of the motivations of businesses to engage in innovation projects. As well as enlightening particular aspects of the challenge, each discipline favours particular methods and frames of investigation which we will explore in further detail for each discipline.

Psychology

The availability of new datasets is not just critical to technological development but has implications on London’s citizens’ daily lives. Data is used to assess and shape daily activities ranging from how people shop, eat, sleep, travel, and learn, to how we choose and maintain relationships. That said, there is scant literature from the field of psychology specifically focused on challenges around data use and management, even though trust plays a central role in the use of data in public services. This section builds upon existing literature in psychology and social trust, focusing on perceptions of risk.

Trust in psychology can be seen as an underlying state that is influenced by both emotions and cognitions, rather than as the output of rational decision making processes (Eiser et al., 2006). Trust plays a key role in the assessment of risk and leads to behaviours that inform willingness amongst individuals when allowing themselves to be vulnerable when faced with states of imperfect knowledge or a lack of control (Borum, 2010). With respect to both public and private services, citizens renounce some control in their own lives by placing positive expectations in the trustee (public and private entities).

The degree to which this vulnerability is both willing and deliberate is, however, contestable. Firstly, citizens are often left with either limited or no choice but to place their trust in public services (e.g. when using public services like the NHS). Secondly, citizens are often do not know the details of how their data is being used or where it is going, as in the use of data in the Cambridge Analytica scandal (Shen, 2016). When data is re-used by third parties, it is impossible to say whether a citizen would consciously consent to the use of their data if they had been fully informed.

Estonia has developed a highly integrated data-based model for most public services including the identity of its citizens, and has succeeded in building a highly trustworthy relationship between public bodies and citizens (Heller, 2017). Arguably, in a population an eighth of the size of London’s, close-knit social networks mean that the role of accountability via reward and punishment becomes higher, affecting perceptions of risk amongst citizens. Yet, beyond psychological perceptions of risk, the high transparency of public bodies in Estonia also appears to raise levels of trust by lowering levels of threat and perceived risk in trusting public bodies (Heller, 2017). In this case, data is readily available to citizens (implying that their records are in their own control), is stored locally, and the government’s degree of openness is demonstrated by the Estonian CIO’s willingness to disclose vulnerabilities in the chips of some identity cards (Heller, 2017). In comparison, the Local Government’s Transparency Code (2015) in London sets out minimum standards of openness, like the requirement to publish expenditures over £500, but even that has been criticized as resource-heavy and unnecessary (Davenport, 2016).

On the other hand, a study conducted by Nicholas Smith et al. (2012) drew upon the amplification of social risk as framed by international scandals. Particularly, the role of trust, risk perceptions and beliefs about climate change were “significantly” dampened by the “release of emails between climate scientists in England and the United States” who criticised the attention being given to climate change (Dawson et al., 2012, p. 816). This indicates how the media plays a central role in framing perceptions and beliefs about risk, and explains why scandals lead to low levels of trust in public bodies.

Ipsos Mori (2014) conducted a study regarding the framing of commercial use of data. 2000 adults aged between 16 and 75 were surveyed, trust deficits were found to correlate strongly to how the explanations about the commercial use of data were framed. While many responses followed the snapshot provided in Figure 3, responses altered from a 33% trust level to 55% when framed in terms of the social benefits of data-collection rather than those focused purely on trust in institutions (Royal Statistical Society, 2014).

This suggests that trust is both a complex and subtle issue, and while it may be related to beliefs, trust may actually follow an associationist model (Eiser et al., 2006) where emotions frame the level of risk individuals are willing to accept. In return, this informs trust and may be easier to control via framing behaviours in a way that promotes both feelings and responsibility. It is however important to note that the study is not necessarily representative of all citizens and may contain a degree of self-reported bias.

Based on perceptions of trust and risk, citizen engagement should be prioritized in the early steps to building any kind of centralised data stores - like the existing but relatively novel London DataStore. The challenges of promoting trust are not limited solely to citizens but also extend to trust between entities such as boroughs, TfL, London Fire Brigade, central government and other partners in innovation.

Social Justice

How can we ensure that the risks and benefits of innovation are justly distributed to all citizens, regardless of their categorisation in society? This is the key concern of social justice with regard to our challenge. We cannot take London’s citizens as one homogenous group, as the people of London are highly diverse. As such, innovation projects will impact each social group to a different extent and in a different way. Harvey (1973) warns that not all social groups are adaptable to change and may suffer disproportionately. Therefore, while it is impossible to fully understand the impact and implications of innovation projects in advance, social justice would ask that LOTI takes and encourages its partner to take sufficient time to consider the impact on people of different races, genders, ages, socio-economic statuses, technological capabilities and abilities.

Additionally, social justice demands that just distribution is achieved through just means. LOTI must therefore ensure that the means that lead to the creation of digital innovation in London do not simply replicate existing unequal power structures between social groups. One way to ensure a just process is to engage members of all social groups in the decision making procedure. Soja (2000) argues that civil rights should include the right of all urban residents to participate in the social production of their city. LOTI should therefore prioritise civic engagement in innovation projects. Engagement could be achieved through a combination of traditional methods of engagement with innovative methods which have been selected for their suitability. LOTI’s priority should be to increase the inclusion of opinions from people who London in change making decisions, particularly where the change could disproportionately affect them due to their position and categorisation in society.

When considering the role LOTI specifically could have in ensuring fair usage of data in innovation projects, social justice suggests two roles for the organisation:

1. Prioritising/facilitating and supporting individual projects that directly tackle social injustices.

2. Promoting good practices and processes both when shaping LOTI’s priorities and on all LOTI projects when it comes to decisions that concern the distribution of resources, risks and benefits.

Firstly, there is great opportunity for the collection and analysis of data to benefit the lives of London’s citizens by delivering better public services to social groups that may not historically have benefitted to the same extent as other citizens. For example, data can be used to help predict when a tenant may fall behind on their rent payment and create the opportunity for support to be provided by the local authority to avoid eviction, as is happening in Hackney (Harrison, 2017). This could reduce financial stress on people with low incomes. However, it should be noted that data is not entirely objective: the inclusion or exclusion of fields can change the story about the people featured or the life of the city (Acuto & Parnell, 2016). As there is no established global consensus on who sets metrics, or generates and monitors data, it is a key social justice concern to ensure fair, accessible, and effective monitoring and mechanisms are established in each city’s data collection policies.

Secondly, the history of rights shows further opportunity for LOTI to help citizens promote social justice in London. According to J.L. Austin’s (1962) performativity theory, the authority of a right comes from articulation of that right; by uttering the right, the right comes into existence and social reality is changed. Performative interpretations of rights can provide a useful framework for considering the authority of rights and who articulates them. However, there are limitations, as Stephanie DeGooyer (2018) highlights: the performance requires an established audience to recognise and validate a rights claim. LOTI could choose as an institution to provide such a forum for citizens to voice and establish their rights in this new age of rapid digital innovation.

To conclude, social justice asks decision makers to not only consider the impact an innovation project may have on people as the same social groups as them, but on people from all social groups, whether or not they have the ability to participate in decision-making. Considering the means of decisions-making and ensuring these processes are as fair as possible is the most preferable way of achieving social justice’s ultimate goal: the just distribution of resources throughout society.

Business

Business plays a crucial role in innovation in urban environments, in particular when it comes to the use of data to improve services. Innovation is key to all aspects of business, and the competitive nature of business incentivises and leads to a thriving culture of innovation (Bessant, 2017, p.1). Almost all innovative projects involving data in cities involve a private partner, whether as a provider in a traditional procurement relationship, all the way up to the level of involvement of Sidewalk Labs in Toronto Waterfront, taking on the roles of placemaking traditionally held by government agencies.

The city environment has traditionally been a place of great business innovation, thriving on the new knowledge available in cities as it ‘spills over’ from research and development activities by universities and other businesses. As a place with immense human resources and available knowledge from universities, London serves as an agglomerative centre for the technology industry, hosting businesses ranging from small startups to the largest companies in the industry, such as Google. This industry facilitates substantial investment into London, having received £4.5bn of investment in the first half of 2017 (London & Partners, 2017). There are therefore great opportunities for greater involvement of the technology industry in public service provision.

In the field of data, data from the public sector is used in innovative ways which benefit business, the public sector and the wider community. For instance, Transport for London has released open data through its APIs, allowing 13,000 developers to innovate with this data. This has led to an estimated £70m benefit to customers due to time saved when using innovative journey planning apps, as well as an estimated contribution of £12m Gross Value Added to the London economy and supporting an estimated 730 jobs in London businesses (Deloitte, 2017, p.20). This is an example of successful collaboration that benefits businesses, citizens and governments alike.

In smart cities, there are opportunities to extend this collaboration with businesses, allowing them to contribute technology to the development of the public realm. Sidewalk Labs’ venture into place-making in the Toronto Waterfront district represents a first venture into this new mode of public service delivery in which business can provide investment and new technologies to tackle the challenges that cities face (Sidewalk Labs, 2018).

3. Urban Experiments

As argued by the disciplines of social justice and psychology, citizen engagement within the decision-making process could ensure a fairer distribution of the risks and benefits of innovation and enhance feelings of trust in public value projects. In addition, citizens may benefit from standards which reassure them that data projects make use of their data in a responsible way.

Below, we outline a novel platform, which is yet to be trialled at city scale, as a way to engage citizens, and a quality mark to be applied to projects which make use of citizens’ data.

Swarm Intelligence

Traditional engagement methods (focus groups, surveys, interviews) make it difficult to fairly represent social groups, owing to the time and money required. However, by using technological innovations, in addition to traditional engagement methods, the level of engagement among London’s multi-million population could be both tested and potentially scaled.

There are many forms of intelligence. However, ‘collective’ or ‘swarm’ intelligence (named after behaviour first observed in bees) is increasingly praised in literature for resulting in optimal decisions.Swarm intelligence occurs when a group makes a decision after negotiating multiple options on the basis that the argument for one option has more weight. While historically the negotiation of preferred outcomes could only take place face-to-face, for example, in focus groups, innovative digital platforms could allow people to engage in such complex negotiations from the comfort of their own homes, as long as they can access a device connected to the internet.

Rosenberg (2015) has developed a novel platform called UNU which enables distributed populations of users with internet access to congregate online in real-time swarms and tackle problems collectively, as an artificial swarm intelligence (ASI).

Large numbers of distributed users can log into a central server from their personal device. At the start of each decision, all users are presented with a question and set of possible answers, displayed graphically. Each possible answer is positioned equidistant from the puck’s initial starting position. The puck functions like a physical puck and has inbuilt properties of mass and friction. Each user has a graphical magnet which they can control with their mouse or touch screen and which can influence the movement of the puck towards an answer. The user can not see other users’ magnets, however can observe in real time the influence of other users in the overall movement of the puck and react by moving their magnet to agree with or oppose the current direction of negotiation. Depending on the question and answers posed, the puck can visit a few answers before settling on a final answer.

The aim of using this technology could be to design urban experiments, where ​LOTI could employ the platform to collect data from London’s citizens on their priorities for data collection and use. Apart from gaining data directly from citizens, LOTI would also be able to use the platform in conjunction with technologies like IoT sensors to test perceptions of trust and risk. By having access to different population groups, comparative analysis may enable deeper insight into what public value means for different groups of citizens (including differentiation by borough). Additionally or alternatively, if LOTI were to support the development of such a platform, it could be an invaluable tool for Boroughs when consulting on the parameters of unique innovation projects.

Example 1.

The UNU platform could be used to pose the following question:

“What is your biggest concern in the collection of your personal data?”

Possible responses could be generated by users or provided by LOTI, for example:

• Cyber-attacks and data theft

• Identity theft

• Feeling watched

• Used for private profit

• Used for undisclosed purposes

• Inaccuracy of data held

The results could feed into LOTI’s development of standards regarding the use of citizens’ personal data, taking their concerns into account so that the standards prioritise citizens’ concerns.

Evaluation

The UNU platform has been used to test human swarm intelligence, with favourable results. 48 trial participants were asked to predict the winners of the 2015 Academy Awards in 15 awards categories. Taking the averages from polls of large numbers of individuals is the standard way of gathering ‘the wisdom of crowds’. This trial aimed to compare the UNU platform to the traditional polling method. The results are as follows:

• Polling: a participant made 6 correct predictions on average when polled individually (40% success)

• Human swarm intelligence: a representative subset consisting of 7 participants used the UNU platform to make 11 correct predictions (73% success)

• Only one of the 48 participants made more than 11 correct predictions when polled.

• While this trial has its limitation in size and application, and can be validated externally by the ‘true’ results, it shows promise for the usefulness of the UNU platform in sourcing correct opinions.

Benefits of UNU:

• Citizens with access to the internet can participate in making decisions that reflect their priorities.

• Cost could be saved long-term in comparison to traditional focus groups which require physical travel to a hired space.

Limitations of UNU:

• Citizens could be excluded based on how comfortable they are using such technologies.

However, support could be provided in libraries or other designated spaces.

• After development, the UNU platform may need to be accompanied with training and/or technical support.

More information available: ​https://unanimous.ai/

Data Quality Mark

While Experiment 1 enhances the ability of LOTI and other data-collecting organisations to understand citizens’ views, there is a need to “close the loop” and provide reassurance to citizens about the use of their data. A quality mark could see LOTI take on a role as an accreditor of projects which use London’s citizens’ data. Drawing on the insights gained from the views of citizens and other stakeholders through Experiment 1 or other means, LOTI would develop a set of standards which govern the use of citizens’ data. Projects which use citizens’ data could then be awarded a quality mark which enhances trust for citizens when they come into contact with an innovative data project. The quality mark would give citizens confidence that their data would be used fairly. Obtaining the mark could also be made a requirement in procurement processes for boroughs.

The data quality mark combines disciplinary perspectives from business, social justice and psychology. It fits in well with existing procurement processes, since businesses can gain a competitive advantage or meet the requirements of a procurement tender by gaining accreditation. Building up the accreditation from citizens’ views allows the mark to further the aims of social justice. Finally, it takes account of the psychological basis of trust and seeks to increase the trust of users as its primary goal.

Similar accreditation schemes exist: an example is ​Fair Data​, a quality mark administered by the Market Research Society which launched in 2013, and is modelled on similar quality marks such as ​Fairtrade and ​Investors in People​. The mark aims to provide reassurance to citizens about the use of their data when it is being collected and used by research organisations, which could be private or public sector organisations (MRS, 2017a; 2017b). The mark can be displayed when collecting data from users as part of a market research process. While the principles behind the Fair Data mark fit well with the aims of this proposal, the mark is not comprehensive in its definition of the standards for the use of data, defining only general principles and relying primarily on self-assessment of organisations prior to accreditation. We would envisage a more thorough assessment of the use of data in projects through a LOTI accreditation process where LOTI would ensure that projects met agreed standards.

London borough councils and other procuring bodies would benefit from a quality mark because it would allow them to specify compliance with a particular standard, rather than having to create their own specification of the standards around the use of citizens’ data. A common set of standards would also increase the scope for co-operation between boroughs and other agencies, since they could be confident in using data collected through projects which others had procured, knowing that the data had been obtained and processed responsibility.

The impact of a quality mark could be assessed by surveying citizens’ trust in projects which used the quality mark compared to those which did not use the mark. The stakeholder consultation tools described earlier could be used for this process, forming a feedback loop where citizens’ views are constantly used to inform and renew the standards for the use of their data. However, the online decision-making platform to engage with the public may not be inclusive of all of the public, particularly those who are not comfortable engaging with online technologies.

The economic viability of the quality mark may be uncertain. Developing the standards would require substantial work from LOTI in consultation with stakeholders, and ongoing engagement and development would be required to ensure that the standards remained up to date with current technologies and practices. Subsequently, the accreditation of each project would incur costs which would ultimately increase the costs of the projects, whether charged to the procuring body or the private sector provider. This results in a long-term economic cost associated with the mark.

City Sharing

The introduction of a quality mark allows for scope for international spread. As previously discussed, the issues of public trust, ethics in the use of data and privacy are of national and international significance, so the mark would be widely applicable. London is well-placed to initiate such a project, which has scope for expansion due to London’s influential position nationally and internationally. The Fair Data mark has been adopted outside the UK in Australia, the Netherlands and Singapore (MRS, 2018), through a model in which market research industry bodies in those countries act as accreditation bodies for the use of the mark. Any expansion of this scheme is likely to involve the use of other agencies to accredit projects taking place outside the London area.

There is precedent for the expansion of accreditation schemes in this way. The Fleet Operator Recognition Scheme (FORS) started out in 2008 as a Transport for London (TfL) scheme in which fleet vehicle operators could apply for accreditation of their vehicles as meeting standards for the safety of other road users (CommercialFleet, 2015). The accreditation process is marketed to fleet operators as a way of differentiating themselves from competitors when bidding for contracts (FORS, 2018), and TfL requires its contractors to be accredited. The scheme moved from a London-only scheme to a national rollout after TfL awarded a contract for the running of the accreditation process to a private sector partner, meaning that TfL is able to take a more “hands-off” approach to the management of the scheme while facilitating expansion. This expansion is beneficial to fleet operators who operate vehicles across the country. The scheme is self-funding thanks to fees charged to participating fleet operators. The expansion has benefited other city governments too: for example, Newcastle has implemented a FORS accreditation scheme as part of a project aimed at developing cleaner urban freight transport (Cossu, 2016).

This model of appointing of a private partner to oversee accreditation, while making the promotion of the mark economically viable, might be a challenging prospect for a mark which aims to leverage public trust in governments to enhance the trust citizens place in partnership projects. In this case, accreditation of projects might be better executed by other government bodies, perhaps at a national level, although these would have to be carefully chosen, owing to the wide variation in public trust in government bodies’ use of citizens’ data.

Conclusion

As evidenced in this report, LOTI has both great potential and great opportunity to raise the level of citizen trust in innovation projects by including citizens’ voices in shaping the guidelines for the processes and innovation projects which both collect and use their data. Psychology shows that opportunities to gain trust exist in language usage and by being a public body rather than private. Social justice suggests that citizen rights can be created after articulation by citizens and acknowledgement by authority. LOTI therefore could be at the forefront of digital rights. However, the expertise of research and development in business is essential to create innovation faster than the public sector norm. Innovation itself could even assist the collection of citizen views with new platforms, such as Unanimous AI. LOTI has enormous potential to create benefits from innovation by bridging the divide between private and public, as long as the voices of citizens are integrated in its development.