More human than human? Artificial intelligence in the archive

ABSTRACT

Not a day appears to go by without breaking news of some Artificial Intelligence (AI) advance that seemingly has the potential to transform our lives. As recordkeeping professionals, we can very well ask, ‘What about us?’ Where is the AI or automation to help us with our classification, appraisal and disposal work? If we are to meet the challenges of managing records in the digital age, such technology – together with appropriate skills and knowledge – will be necessary. How can AI automate our digital recordkeeping and archive work? In this article, the authors provide a snapshot of the practice of AI in Australian recordkeeping. What is the reality versus the hype of such technology, and what is actually being done now? In answering these questions, they first provide a brief introduction into AI techniques and their characteristics in relation to recordkeeping work. They then introduce four case studies from Australian archival and government institutions that have embarked on AI initiatives. In each case, they provide an overview of the project in terms of requirements, activities to date, outcomes and futures. The article concludes with a discussion of the lessons learnt, issues and implications of AI in the archive.

Acknowledgments

The authors would like to acknowledge their project collaborators, without whom these initiatives would not have been possible: Richard Lehane and Malay Sharma – NSW State Archives and Records; David Hearder, Marian Kearney and Sean Wright – National Archives of Australia; and John Machin and Wicka Simet – Commonwealth Government, Department of Finance.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Stanford University, ‘Artificial Intelligence and Life in 2030: One Hundred Year Study on Artificial Intelligence; Report of the 2015 Study Panel’, 2016, available at <https://ai100.stanford.edu/sites/default/files/ai_100_report_0916fnl_single.pdf>, accessed 14 February 2018.

2 National Archives of Australia and Council of Australasian Archives and Records Authorities, ‘Digital Archiving in the 21st Century: Archives Domain Discussion Paper’, Collections Council of Australia, 2006; Frank Upward, Barbara Reed, Gillian Oliver and Joanne Evans, ‘Recordkeeping Informatics: Re-Figuring a Discipline in Crisis with a Single Minded Approach’, Records Management Journal, vol. 23, no. 1, 2013, pp. 37–50, doi:10.1108/09565691311325013.

3 Ross Harvey and Dave Thompson, ‘Automating the Appraisal of Digital Materials’, Library Hi Tech, vol. 28, no. 2, 2010, pp. 313–22, doi:10.1108/07378831011047703.

4 John McDonald, ‘Managing Records in the Modern Office: Taming the Wild Frontier’, Archivaria, no. 39, Spring 1995, pp. 70–9.

5 Kate Cumming and Anne Picot, ‘Reinventing Appraisal’, Archives and Manuscripts, vol. 42, no. 2, 2014, pp. 133–45, doi:10.1080/01576895.2014.926824.

6 The National Archives UK, ‘Digital Strategy’, available at <https://www.nationalarchives.gov.uk/documents/the-national-archives-digital-strategy-2017-19.pdf>, accessed 14 February 2018.

7 Anne Gilliland, ‘Archival Appraisal: Practising on Shifting Sands’, in Caroline Brown (ed.), Archives and Recordkeeping: Theory into Practice, Facet, London, 2014, p. 50. Attributed to Clive Humby and, perhaps, first quoted in Michael Palmer, ‘Data is the New Oil’, ANA Marketing Maestros, 2006, available at <http://ana.blogs.com/maestros/2006/11/data_is_the_new.html>, accessed 14 February 2018; Upward et al., ‘Recordkeeping Informatics’.

8 Cassie Findlay, ‘Appraisal: An Essential Tool for Digital Recordkeeping’, August 2015, available at <http://www.informationstrategy.tas.gov.au/Publications/Documents/Cassie-Findlay-Appraisal-Essential-tool-for-digital-recordkeeping-Aug-2015.pptx>, accessed 14 February 2018.

9 Frank Upward, Barbara Reed, Gillian Oliver and Joanne Evans, Recordkeeping Informatics for a Networked Age, Social Informatics, Monash University Publishing, Clayton, Vic., 2018, pp. xix–xx.

10 William Vinh-Doyle, ‘Appraising Email (Using Digital Forensics): Techniques and Challenges’, Archives and Manuscripts, vol. 45, no. 1, 2017, pp. 18–30, doi:10.1080/01576895.2016.1270838.

11 Anthony Cocciolo, ‘Finding Inactive Records on Institutional Networks: An Evaluation of Tools’, Practical Technology for Archives, June 2016, available at <https://practicaltechnologyforarchives.org/issue6_cocciolo/>, accessed 14 February 2018.

12 Victoria Sloyan, ‘Born-Digital Archives at the Wellcome Library: Appraisal and Sensitivity Review of Two Hard Drives’, Archives and Records, vol. 37, no. 1, 2016, pp. 20–36, doi:10.1080/23257962.2016.1144504.

13 Ross Spencer, ‘Binary Trees? Automatically Identifying the Links Between Born-Digital Records’, Archives and Manuscripts, vol. 45, no. 2, 2017, pp. 77–99, doi:10.1080/01576895.2017.1330158.

14 Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, AK Peters, Natick, MA, 2004, p. 523.

15 Stanford University, p. 12.

16 Edward Feigenbaum as quoted in McCorduck, p. 326.

17 Richard Bellman, An Introduction to Artificial Intelligence: Can Computers Think? Boyd & Fraser, San Francisco, 1978, p. 3.

18 Stanford University.

19 Gartner, ‘Hype Cycle Research Methodology Gartner Inc.’, 2018, available at <https://www.gartner.com/technology/research/methodologies/hype-cycle.jsp>, accessed 14 February 2018.

20 McCorduck, Machines Who Think, 423.

21 Ikujiro Nonaka and Hirotaka Takeuchi, The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York, 1995, pp. 86, 154.

22 Anne J Gilliland. ‘Designing Expert Systems for Archival Evaluation and Processing of Computer-Mediated Communications’, in Anne J Gilliland, Sue McKemmish and Andrew J Lau (eds), Research in the Archival Multiverse, Monash University Publishing, Clayton, Vic., 2016, pp. 686–722. For more detail, seek access to Anne Jervois Gilliland-Swetland, ‘Development of an Expert Assistant for Archival Appraisal of Electronic Communications: An Exploratory Study’, PhD dissertation, University of Michigan, 1995.

23 Daniel G Bobrow, Sanjay Mittal and Mark J Stefik, ‘Expert Systems: Perils and Promise’, Communications of the ACM, vol. 29, no. 9, 1986, pp. 880–94.

24 Craig Stanfill and David Waltz, ‘Toward Memory-Based Reasoning’, Communications of the ACM, vol. 29, no. 12, 1986, pp. 1213–28.

25 Herbert Gelerntner, ‘Realization of a Geometry-Theorem Proving Machine’, in Edward A Feigenbaum and Julian Feldman (eds), Computers and Thought: A Collection of Articles, McGraw-Hill, New York, 1963, pp. 134–52.

26 Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2017, p. 2, available at <http://www.deeplearningbook.org/>, accessed 14 February 2018.

27 ibid., p. 3.

28 ibid., p. 98.

29 Steven Bird, Ewan Klein and Edward Loper, Natural Language Processing with Python, O’Reilly Media, Sebastopol, CA, 2016, p. 221.

30 Goodfellow, Bengio and Courville, p. 103.

31 ibid.

32 The following names mentioned in this article are registered trademarks^(TM): IBM Watson, Jeopardy, Google Street View, Facebook, IBM Lotus Notes, Nuix, Microsoft Azure, Microsoft Azure Cognitive Services, Python, Objective, Amazon Web Services, Microsoft Exchange, Microsoft Office, Netflix, Apache Spark and Scala

33 David Ferrucci et al., ‘Building Watson: An Overview of the DeepQA Project’, AI Magazine, vol. 31, no. 3, 2010, pp. 59–79; Jo Best, ‘IBM Watson: The Inside Story of How the Jeopardy-Winning Supercomputer Was Born, and What It Wants to Do Next’, Tech Republic, September 2013, available at <https://www.techrepublic.com/article/ibm-watson-the-inside-story-of-how-the-jeopardy-winning-supercomputer-was-born-and-what-it-wants-to-do-next/>, accessed 14 February 2018.

34 Goodfellow, Bengio and Courville, p. 3.

35 Ian Goodfellow et al., ‘Multi-Digit Number Recognition from Street View Imagery Using Deep Convolutional Neural Networks’, arXiv Preprint arXiv:1312.6082, 2013; Li Deng and Dong Yu, ‘Deep Learning: Methods and Applications’, Foundations and Trends in Signal Processing, vol. 7, nos. 3–4, 2013, p. 219, doi:10.1561/2000000039.

36 Gary Marcus, ‘Deep Learning: A Critical Appraisal’, arXiv Preprint arXiv:1801.00631, January 2018.

37 Goodfellow, Bengio and Courville, Deep Learning, 6.

38 Stanford University; Goodfellow, Bengio and Courville, pp. 22–5.

39 Cade Metz, ‘AI is Transforming Google Search. The Rest of the Web Is Next’, WIRED, February 2016, available at <https://www.wired.com/2016/02/ai-is-changing-the-technology-behind-google-searches/>, accessed 14 February 2018.

40 Goodfellow, Bengio and Courville, p. 20.

41 Goodfellow et al.

42 Chen Sun et al., ‘Revisiting Unreasonable Effectiveness of Data in Deep Learning Era’, paper presented at IEEE, Venice, 22–29 October 2017.

43 Goodfellow, Bengio and Courville, pp. 21–3.

44 Davide Castelvecchi, ‘Can We Open the Black Box of AI?’ Nature News, vol. 538, no. 7623, October 2016, pp. 20–3, doi:10.1038/538020a.

45 For a discussion of the effects of these issues, see Joanna Redden and Jessica Brand, ‘Data Harm Record’, December 2017, available at <https://datajusticelab.org/data-harm-record>, accessed 14 February 2018; and Cathy O’Neil, Weapons of Math Destruction, Penguin Books, New York, 2017. For initiatives that are looking at ways of addressing these problems, see FAT/ML, ‘Home:: FAT ML’, Fairness, Accountability, and Transparency in Machine Learning, 2017, available at <https://www.fatml.org/>, accessed 14 February 2018; Paul VoosenJul, ‘How AI Detectives Are Cracking Open the Black Box of Deep Learning’, Science AAAS, July 2017, available at <http://www.sciencemag.org/news/2017/07/how-ai-detectives-are-cracking-open-black-box-deep-learning/>, accessed 14 February 2018.

46 Robin Jia and Percy Liang, ‘Adversarial Examples for Evaluating Reading Comprehension Systems’, arXiv Preprint arXiv:1707.07328, 2017.

47 Marcus.

48 André Vellino et al., ‘Assisting the Appraisal of E-Mail Records with Automatic Classification’, Records Management Journal, vol. 26, no. 3, 2016, pp. 293–313, doi:10.1108/RMJ-02–2016-0006; Floriana Esposito et al., ‘Machine Learning Methods for Automatically Processing Historical Documents: From Paper Acquisition to XML Transformation’, paper presented at IEEE, Palo Alto, CA, 23–24 January 2004, doi:10.1109/DIAL.2004.1263262.

49 Kye O’Donnell, ‘Taming Digital Records with the Warrior Princess: Developing a Xena Preservation Interface for TRIM’, Archives and Manuscripts, vol. 38, no. 2, 2010, pp. 37–60.

50 Susan Leavy, Emilie Pine and Mark Keane, ‘Mining the Cultural Memory of Irish Industrial Schools Using Word Embedding and Text Classification’, paper presented at DH2017, Montreal, Canada, 8–11 August 2017, available at <https://dh2017.adho.org/abstracts/098/098.pdf/>, accessed 14 February 2018.

51 IBM, ‘Auto-Classification Models’, IBM, 2014, available at <https://www.ibm.com/support/knowledgecenter/SSDUBN_7.5.1/Administrator/cpt/cpt_autoclassificationmodel.html>, accessed 14 February 2018; Open Text, ‘Auto-Classification for Records Management’, OpenText, 2018, available at <https://www.opentext.com.au/what-we-do/products/discovery/auto-classification>, accessed 14 February 2018; Integro, ‘Auto-Classification – Integro: Experts in Information Governance and Enterprise Content Management’, 2017, available at <https://www.integro.com/ecm-solutions/auto-classification>, accessed 14 February 2018; Concept Searching, ‘Auto-Classification, Taxonomy Management, Metadata Generation’, Concept Searching, 2017, available at <https://www.conceptsearching.com/>, accessed 14 February 2018.

52 Jason R Baron, ‘Toward a Federal Benchmarking Standard for Evaluating Information Retrieval Products Used in E-Discovery’, Sedona Conference Journal, vol. 6, Fall 2005, pp. 237–9.

53 Nicholas Fripp, ‘Is Machine Learning the Future of Records Management?’ IQ: The RIM Quarterly, vol. 33, no. 1, 2017, pp. 22–3; George Parapadakis, ‘A Clouded View of Records and Auto-Classification’, For What It’s Worth…, June 2013, available at <https://4most.wordpress.com/2013/06/26/clouded-view-of-records-and-classification/>, accessed 14 February 2018; Ronald Layel, ‘Auto-Classification for RM – Beginning to See It as Possible – Association for Information and Image Management International’, February 2012, available at <http://community.aiim.org/blogs/ron-layel/2012/02/16/auto-classification-for-rm-–-beginning-to-see-it-as-possible>, accessed 14 February 2018.

54 Tim Shinkle, ‘Automated Electronic Records Management? Are We There yet? IDM Magazine’, Image and Data Manager, December 2016, available at <http://idm.net.au/article/0011369-automated-electronic-records-management-are-we-there-yet>, accessed 14 February 2018.

55 Parapadakis.

56 For example, see Department of Corporate and Information Services, ‘Guidelines for the Development of a Functional Records Disposal Schedule – Records Policy and Standards – NTG IT and Communications – Department of Corporate and Information Services,’ Northern Territory Government, 2014, available at <http://www.nt.gov.au/dcis/info_tech/records_policy_standards/records_disposal/index.shtml>, accessed 16 April 2018; Public Record Office Victoria, ‘Retention and Disposal Authorities (RDAs)’, PROV, 2018, available at <https://www.prov.vic.gov.au/recordkeeping-government/how-long-should-records-be-kept/retention-and-disposal-authorities-rdas>, accessed 16 April 2018; Queensland State Archives (Department of Science, Information Technology and Innovation), ‘Use a Retention and Disposal Schedule’, Queensland Government, 2017, available at <https://www.forgov.qld.gov.au/use-retention-and-disposal-schedule>, accessed 16 April 2018; State Archives and Records, ‘Disposal Authorisation Procedures’, State Archives and Records NSW, 2015, available at <https://www.records.nsw.gov.au/recordkeeping/rules/procedures/disposal-authorisation>, accessed 16 April 2018.

57 Erika Morphy, ‘How to Differentiate Machine Learning from Dressed-up BI’, CMSWire.com, January 2018, available at <https://www.cmswire.com/digital-experience/how-to-differentiate-machine-learning-from-dressed-up-bi/>, accessed 14 February 2018.

58 Parapadakis.

59 Harold Greene, ‘United States V. Poindexter, 725 F. Supp. 13 (D.D.C. 1989)’, Justia Law, October 1989, available at <https://law.justia.com/cases/federal/district-courts/FSupp/725/13/1406938/>, accessed 14 February 2018.

60 Goodfellow, Bengio and Courville, p. 141.

61 Tom Simonite, ‘Machines Learn a Biased View of Women’, WIRED, August 2017, available at <https://www.wired.com/story/machines-taught-by-photos-learn-a-sexist-view-of-women/>, accessed 14 February 2018.

62 Brian d’Alessandro, Cathy O’Neil and Tom LaGatta, ‘Conscientious Classification: A Data Scientist’s Guide to Discrimination-Aware Classification’, Big Data, vol. 5, no. 2, 2017, pp. 120–34, doi:10.1089/big.2016.0048.

63 Shinkle; Parapadakis.

64 Bitcurator Consortium, ‘BitCurator NLP’, Bitcurator, 2018, available at <https://bitcurator.net/bitcurator-nlp/>, accessed 14 February 2018.

65 Hui Han et al., ‘Automatic Document Metadata Extraction Using Support Vector Machines’, paper presented at IEEE, Houston, TX, 27–31 May 2003.

66 Tami Deedrick, ‘It’s Technical, Dear Watson’, IBM Systems Magazine, February 2011, available at <http://ibmsystemsmag.com/ibmi/trends/whatsnew/it’s-technical,-dear-watson/>, accessed 14 February 2018.

67 And not just AI. GPUs are also being employed in cryptographic applications, most notoriously in the mining of virtual currencies.

68 Layel; Vellino et al.; Goodfellow et al.

69 Goodfellow, Bengio and Courville, p. 108.

70 Judy Sheard, ‘Quantiative Data Analysis’, in Kirsty Williamson and Graeme Johanson (eds), Research Methods: Information, Systems and Contexts, Tilde University Press, Prahran, Vic., 2013, p. 408.

71 Baron; Richard J Cox, ‘The Documentation Strategy and Archival Appraisal Principles: A Different Perspective’, Archivaria, vol. 38, Fall 1994, pp. 11–36.

72 Philip Hider and Ross Harvey, Organising Knowledge in a Global Society: Principles and Practice in Libraries and Information Centres, Topics in Australasian Library and Information Studies 29, Centre for Information Studies, Charles Sturt University, Wagga Wagga, 2008, p. 191.

73 Goodfellow, Bengio and Courville, p. 418.

74 ibid., p. 419.

75 Goodfellow et al.

76 Eugene Yang, David Grossman, Ophir Frieder and Roman Yurchak, ‘Effectiveness Results for Popular E-Discovery Algorithms’, paper presented at the 16th International Conference on Artificial Intelligence and Law, London, 12–16 June 2017.

77 Gordon V Cormack and Maura R Grossman, ‘Evaluation of Machine-Learning Protocols for Technology-Assisted Review in Electronic Discovery’, ACM, 2014, pp. 153–62, doi:10.1145/2600428.2609601. Also see Baron; Yang et al.

78 See Nuix, ‘Electronic Discovery’, Investigation, Cybersecurity, Information Governance and eDiscovery Software, 2017, available at <https://www.nuix.com/problems-we-solve/electronic-discovery>, accessed 14 February 2018.

79 For discussion of this method, see Spencer.

80 Glen Humphries, ‘Machine Learning and Records Management’, 14 September 2014, 2017, available at <http://futureproof.records.nsw.gov.au/machine-learning-and-records-management/>, accessed 2 March 2018.

81 At the time of the pilot, Microsoft’s Trust Center included the following advice about use of its Cognitive services: ‘Data that is sent to Cognitive Services is treated differently than other customer data. Microsoft may use Cognitive Services data to improve Microsoft products and services. For example, we may use content that you provide to the Cognitive Services to improve our underlying algorithms and models over time. To do that, we may retain Cognitive Services data after you are no longer using the services.’ An additional clause, under the Privacy tab, stated: ‘Cognitive Services collect and use many types of data, such as images, audio files, video files, or text, all of which may be retained by Microsoft indefinitely to improve Microsoft products and services, without a means for you to access or delete that retained data. Unless otherwise stated in documentation for a particular service, these services provide no means for you to store, access, extract, or delete customer data.’

82 State Records Authority of New South Wales, ‘Transferring Records Out of NSW (GA35)’, State Archives and Records NSW, November 2015, available at <https://www.records.nsw.gov.au/node/649>, accessed 14 February 2018.

83 State Records Authority of New South Wales, ‘Storage of State Records with Service Providers Outside of NSW’, State Archives and Records NSW, November 2015, available at <https://www.records.nsw.gov.au/recordkeeping/advice/storage-and-preservation/service-providers-outside-nsw>, accessed 14 February 2018.

84 Scikit-learn developers, ‘Scikit-Learn: Machine Learning in Python’, 2017, available at <http://scikit-learn.org/stable/>, accessed 14 February 2018. Also see Fabian Pedregosa et al., ‘Scikit-Learn: Machine Learning in Python’, Journal of Machine Learning Research, vol. 12, October 2011, pp. 2825–30.

85 The actual machine specifications were: HP Z440 Workstation, CPU: 2x Intel® Xeon® E5-1650 v3 (12 cores total), RAM: 64 GB DDR4, Storage: 2x Micron M600 1 TB SSD.

86 State Records Authority of New South Wales, ‘Administrative Records (GA28)’, State Archives and Records NSW, November 2015, available at <https://www.records.nsw.gov.au/recordkeeping/rules/gdas/ga28>, accessed 14 February 2018.

87 Formally, the Inverse Document Frequency = Log (Total number of documents/Number of documents having the particular word).

88 W3C, ‘PROV-DM: The PROV Data Model’, 2013, available at <http://www.w3.org/TR/2013/REC-prov-dm-2013043>, accessed 2 March 2018. International Council on Archives, ‘Records in Contexts: A Conceptual Model for Archival Description’, 2016, available at <https://www.ica.org/sites/default/files/RiC-CM-0.1.pdf>, accessed 2 March 2018.

89 Available at <https://www.finance.gov.au/archive/policy-guides-procurement/interoperability-frameworks/information-interoperability-framework>, accessed 15 July 2018.

90 Matt Ridley, ‘Amara’s Law’, November 2017, available at <http://www.rationaloptimist.com/blog/amaras-law/>, accessed 14 February 2018.

91 The identification and classification of ‘duplicates’ is not without its tensions. See Geoffrey Yeo, ‘Nothing Is the Same as Something Else: Significant Properties and Notions of Identity and Originality’, Archival Science, vol. 10, no. 2, 2010, pp. 85–116, doi:10.1007/s10502-010–9119-9.

92 BitCurator Consortium; Spencer.

93 ‘Preparing and Architecting for Machine Learning’, Gartner, January 2017, available at <https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/preparing_and_architecting_for_machine_learning.pdf>, accessed 14 February 2018.

94 Richard Marciano et al., ‘Archival Records and Training in the Age of Big Data’, in Johnna Percell, Lindsay Sarin, Paul Jaeger and John Bertot (eds), Re-envisioning the MLS: Perspectives on the Future of Library and Information Science Education, Advances in Librarianship 44, Emerald Group Publishing, Bingley, UK, 2017, p. 210.

95 Upward et al., ‘Recordkeeping Informatics’.

96 International Organization for Standardization, ISO 23081 Information and Documentation – Records Management Processes – Metadata for Records – Principles, International Organization for Standardization, 2006.

Additional information

Notes on contributors

Gregory Rolan

Dr Gregory Rolan is currently a research fellow at the Centre for Organisational and Social Informatics at Monash University. Following a 30-year career in IT, Dr Rolan returned to study, obtaining a PhD in recordkeeping informatics, investigating participatory recordkeeping systems. His research comprises the design-science study of systems interoperability; conceptual modelling in recordkeeping informatics; metadata standards setting; and organisational/social factors in information systems design and implementation.

Glen Humphries

Glen Humphries is currently a project officer with the Digital State Archive at NSW State Archives and Records. Glen has previously worked for Archives New Zealand from 2006 to 2014 where he gained a wide knowledge of archival practices before moving to Australia in 2014. Glen joined the Digital State Archive in August 2015 and has been working on a number of digital transfers of various sizes and ages. Glen also currently has been leading pilot projects that look at the capabilities of machine-learning technologies and records management specifically at disposal of structured and unstructured data.

Lisa Jeffrey

Lisa Jeffrey is a Melbourne-based information professional recently engaged at Public Record Office Victoria on a machine-assisted appraisal Proof of Concept. She has over 10 years experience as a records manager, archivist and information strategist across the private and public sectors (both Federal and State) and has completed graduate and postgraduate study at Monash University. Lisa is interested in how and why individuals self-document (or do not) in different contexts and how technology can support organisational and community memory.

Evanthia Samaras

Evanthia Samaras has worked in the Australian archive sector since 2013 and is currently the Victorian Electronic Records Strategy, Senior Officer at Public Record Office Victoria. She is presently undertaking a PhD at the University of Technology Sydney to explore how computer-generated imagery projects produced by the film visual effects industry can be archived and preserved for future use.

Tatiana Antsoupova

Tatiana Antsoupova is the acting Chief Information Governance Officer at the National Archives of Australia in Canberra. She has been working at the National Archives since 2005 and was Archives Officer at the Noel Butlin Archives Centre of the Australian National University from 1996 to 2005. Before that, she was a government archivist in Russia. She has a degree in archives and history from the Moscow State Institute of Archival and Historical Studies (now Moscow State University of Humanities) and spent one year at the University of Pittsburgh studying archives and records management with Professor Richard Cox.

Katharine Stuart

Katharine Stuart works for the Australian Department of Finance as the project lead for the Australian Government Records Interoperability Framework. This framework adopts linked data standards to create a semantically interoperable environment for government records. Katharine has previously worked at the National Archives of Australia where she contributed to the development of records management standards, policy and strategy. At the National Archives Katharine led the project team which delivered the Digital Continuity 2020 Policy. Prior to the National Archives, Katharine worked for the State Records Authority of New South Wales on the NSW digital strategy Future Proof. Katharine is a PhD candidate at the University of Canberra, undertaking research into digital government and the effects on records management. Katharine has previous degrees from the University of Canberra and Macquarie University including a Master of Knowledge Management (Information Studies) and Master of Museum Management.