Striking the Balance between Data Innovation and Responsible Use of Data
Governments and development organisations nowadays are turning to new and non-traditional data sources to address some of the most complex challenges that our society faces. Beyond the availability of these emerging data sets, data innovation also requires certain tools, technical capacity and creativity — and more importantly, a purposeful discourse beforehand about what problems these innovations aim to solve and what changes they may effect. We recently hosted a diverse and enthusiastic group of colleagues from Aga Khan Foundation, Australian Department of Foreign Affairs and Trade (DFAT), InnovationXchange, The Ethics Centre and Roshan Telecom for a three-day data innovation clinic at the Lab. While the energy and synergies from the clinic cannot be fully captured in a blog, here we attempt to unpack a few takeaways.
Pulse Lab Jakarta designs, runs and facilitates data innovation clinics every so often, and contextualises each of them to the specific needs of our partners. One amazing part of it is that: no two data innovation clinics are the same. For instance, while our previous data innovation clinics with Bappenas and more recently one we co-organised with Development CAFE were all guided by our idea-to-implementation data innovation toolkit, the knowledge exchanged and discussions among the participants offered something different. Focused on women empowerment this time around, the data innovation clinic demonstrated that this remains to be true.
Data Innovation Can Benefit from Shared Knowledge and Expertise
We transformed our space at the Lab into several lounges to encourage the participants to move about and mingle with our team, as well as engage in different interactive sessions. Apart from exploring some of our flagship data innovation tools, the participants were able to hear first-hand from some members of our team about the tools’ innovation life cycle and opportunities and challenges for uptake. These discussions were useful to get them thinking about some of the problems they were also hoping to address.
Drawing from the data innovation toolkit, the participants started out with a brainstorming session to define the problem and then went on to map the data journey. In doing so, conversations around identifying relevant data sources that might be useful for their context; assessing the quality of data and biases; and wholehearted discussions about data privacy and data protection became centrestage. Coming full circle for a panel discussion on ethics in data innovation, however, was perhaps the highlight of the clinic. Participants had a chance to engage directly with Dr. Simon Longstaff, Executive Director at the Ethics Centre; Sriganesh Lokanathan, Big Data Team Lead at LirneAsia; Derval Usher, Manager of Pulse Lab Jakarta.
Ethics: Beyond A Compliance Checklist
The discussion on ethics underscored the importance of ensuring a balance between data innovation and responsible use of data. The panellists emphasised the importance of spending time to establish ‘why’ an innovation project should be undertaken and outline its intentions for greater social good. Pulse Lab Jakarta’s Manager highlighted that central to the Lab’s work around big data analytics and developing prototypes is our mission to ensure the responsible use of big data and artificial intelligence as a public good. The two focuses are intertwined and not mutually exclusive.
Committing to data innovation projects should not just be about being legally compliant; being ethically sound should also be non-negotiable. This was a critical part of the discussion, raising questions about what happens when data privacy and data protection laws don’t keep pace with digital innovation or in cases where such laws don’t even exist. Dr. Longstaff from the Ethics Centre lent his expertise in this area, by underlining that even with such regulations in place — pursuing an innovation project is not about making sure everything is ticked off on a checklist. Continuous discussions about ethics, he further asserted, should be woven holistically through the innovation process, including making sure that the necessary technical resources are available and preparing for any negative unintended consequences — and should those situations occur, how can they be quickly address both legally and ethically.
Data Innovation Is Not Linear
The emphasis on data literacy resonated throughout the data innovation clinic. But as many of the participants rightly observed, data literacy does not end with simply learning to read, understand and communicate about data; it involves understanding the situational context, becoming acquainted with the various players in the data ecosystem and reminding oneself that real people are at the heart of data innovation. For this reason, our data innovation clinics are always designed together with our partners to ensure their needs are fully understood. We also make it a priority to have a fair balance in terms of the perspectives of the clinic’s facilitators, mixing data experts, human centered design researchers and policy experts from our team.
The data innovation clinic highlighted that data innovation is not a linear process, and our experience working with various development partners and stakeholders in Indonesia and across the region have taught us to be flexible and agile. However, to manage whatever risks, harms and benefits that may be associated with using new and non-traditional data for innovation in the humanitarian and development contexts, we always perform an internal rigorous assessment. During the clinic we shared some of the guiding checkpoints that exist as part of this assessment, which include examining: the ‘Data Type’ involved in the data analytics process, the ‘Risks and Harms’ of data use, the mode and legitimacy of ‘Data Access’, the ‘Data Use’, the adequacy of ‘Data Security’, the adequate level of ‘Communication and Transparency’ and the due diligence on engagement of ‘Third Parties’. But as suggested in the ethics discussion, part of the innovation process may mean pausing midway during an innovation cycle to reassess the situation before moving forward or abandoning a project altogether.
We were extremely delighted to host this exciting group of participants at the Lab, and grateful to them for generously sharing their wealth of knowledge and expertise with our team. Their diverse experiences from the private sector, government and the development sector made this data innovation clinic distinct in its own way, and also gave us an opportunity to understand the different contexts in which data innovation happens and how we need to adapt our mindset and processes accordingly. We were heartened to receive positive feedback and understand that our experiences, hard-won over the past five years of operations, have value with others and we are more than happy to share these experiences.
Pulse Lab Jakarta is grateful for the generous support from the Government of Australia.
