1 Introductions Oscar A. Garca and Prashanth Kotturi
2 Evaluation and the Sustainable Development Goals: Opportunities and Constraints Marco Segone
3 Information and Communication Technologies for Evaluation (ICT4Eval): Theory and Practice Oscar A. Garca, Jyrki Pulkkinenand and Prashanth Kotturi
A Data collection: Faster, cheaper, more accurate
B Data analysis: The machine learning revolution
C Dissemination and learning: Reaching a global audience
D Case studies: Geospatial analysis in environmental evaluation Juha Ilari Uitto, Anupam Anand and Geeta Batra; Simulated field visits in fragile and conflict environments: Reaching the most insecure areas of Somalia virtually Monica Zikusooka; Analysing stories of change: engaging beneficiaries to make sense of data Michael Carbon and Hamdi Ahmedou; Is there a role for machine learning in the systematic review of evidence? Edoardo Masset; Using machine learning to improve impact evaluations Paul Jasper; Using geospatial data to produce fine-scale humanitarian maps Gaurav Singhal, Lorenzo Riches and Jean-Baptiste Pasquier; Using and sharing real-time data during fieldwork Simone Lombardini and Emily Tomkys
E ICTs in practice â" the case for cautious optimism
F Evaluation 2.0; turning dilemmas to dividends?
4 Big data analytics and development evaluation: Optimism and caution, Michael Bamberger
A Some themes from the big data literature
B Demystifying big data
C Where is the big data revolution headed?
D Does big data apply to development evaluation? Should evaluators care about it?
E The great potential for integrating big data into development evaluation
F Big data and development evaluation: the need for caution
G Overcoming barriers to big data use in evaluation
5 Technology, Biases and Ethics: Exploring the Soft Sides of Information and Communication Technologies for Evaluation (ICT4Eval), Linda Raftree
A Factors affecting information technology access and use among the most vulnerable
B Data and technology alone cannot ensure inclusion
C Inclusiveness of access and use affect the representativeness of big data
D Bias in big data, artificial intelligence and machine learning
E Protecting data subjectsâ rights in tech-enabled, data-led exercises
F Improving data privacy and protection in the development sector
6 Technology and its implications for nations and development partners Oscar A. Garca and Prashanth Kotturi
A Structural transition and pathways for economic development
B Who has technology affected the most?
C A Ludditeâs nightmare or a passing phenomenon?
D Implications for sustainable rural development
E Dealing with disruptions and moving forward
F Implications for development partners
7 Conclusions Oscar A. Garca and Prashanth Kotturi