Frontiers in Service Science: The Management of Data Analytics Services: New Challenges and Future Directions

Published Online:https://doi.org/10.1287/serv.2020.0262

References

  • Abis S (2017) Man vs. machine: Quantitative and discretionary equity management. Working paper, Columbia University, New York.Google Scholar
  • Acimovic J, Graves S (2014) Making better fulfillment decisions on the fly in an online retail environment. Manufacturing Service Oper. Management 17(1):34–51.LinkGoogle Scholar
  • Amershi S, Cakmak M, Knox WB, Kulesza T (2014) Power to the people: The role of humans in interactive machine learning. AI Magazine 35(4):105–120.CrossrefGoogle Scholar
  • Ang E, Kwasnick S, Bayati M, Plambeck EL, Aratow M (2015) Accurate emergency department wait time prediction. Manufacturing Service Oper. Management 18(1):141–156.LinkGoogle Scholar
  • Angwin J, Larson J, Mattu S, Kirchner L (2017) Machine bias: There’s software used across the country to predict future criminals and it’s biased against blacks. Accessed August 10, 2020, www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.Google Scholar
  • Arslanian H, Fischer F (2019) Future trends in artificial intelligence. Arslanian H, Fischer F, eds. The Future of Finance: The Impact of FinTech, AI, and Crypto on Financial Services (Springer International Publishing, Cham, Switzerland), 231–247.CrossrefGoogle Scholar
  • Ashlagi I, Roth A (2020) Kidney exchange: an operations perspective. Working paper, Stanford University, Stanford, CA.Google Scholar
  • Aswani A, Shen ZJM, Siddiq A (2019) Data-driven incentive design in the medicare shared savings program. Oper. Res. 67(4):1002–1026.Google Scholar
  • Autor DH (2015) Why are there still so many jobs? The history and future of workplace automation. J. Econom. Perspective 29(3):3–30.CrossrefGoogle Scholar
  • Avrahami A, Herer YT, Levi R (2014) Matching supply and demand: Delayed two-phase distribution at yedioth group: Models, algorithms, and information technology. Interfaces 44(5):445–460.LinkGoogle Scholar
  • Ban GY, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.LinkGoogle Scholar
  • Banko M, Brill E (2001) Scaling to very very large corpora for natural language disambiguation. Proc. 39th Annual Meeting Association Computational Linguistics (Association for Computational Linguistics, Stroudsburg, PA), 26–33.Google Scholar
  • Baris A, Freidewald J, Randa AC (2017) Organ Transplantation. Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Bastani H, Bayati M (2020) Online decision-making with high-dimensional covariates. Oper. Res. 68(1):76–294Google Scholar
  • Benjamin R (2019) Race After Technology: Abolitionist Tools for the New Jim Code (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev. 53(3):464–501.CrossrefGoogle Scholar
  • Bertsimas D, Farias VF, Trichakis N (2013) Fairness, efficiency, and flexibility in organ allocation for kidney transplantation. Oper. Res. 61(1):73–87.LinkGoogle Scholar
  • Bertsimas D, Kallus N, Weinstein AM, Zhuo YD (2017) Personalized diabetes management using electronic medical records. Diabetes Care 40(2):210–217.CrossrefGoogle Scholar
  • Blanchet J, Gallego G, Goyal V (2016) A Markov Chain approximation to choice modeling. Oper. Res. 64(4):886–905.LinkGoogle Scholar
  • Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and Regression Trees (CRC Press, Boca Raton, FL).Google Scholar
  • Bruha I, Famili F (2000) Postprocessing in machine learning and data mining. SIGKDD Exploration 2(2):110–114.CrossrefGoogle Scholar
  • Coase RH (1995) Essays on Economics and Economists (University of Chicago Press, Chicago).Google Scholar
  • Cohen MC, Kalas JJ, Perakis G (2020) Promotion optimization for multiple items in supermarkets. Management Sci., ePub ahead of print June 20, https://doi.org/10.1287/mnsc.2020.3641.Google Scholar
  • Cohen MC, Leung NHZ, Panchamgam K, Perakis G, Smith A (2017) The impact of linear optimization on promotion planning. Oper. Res. 65(2):446–468.LinkGoogle Scholar
  • Cohen-Hillel T, Panchamgam K, Perakis G (2019a) Bounded memory peak-end models can be surprisingly good. Working paper, MIT, Cambridge, MA.Google Scholar
  • Cohen-Hillel T, Panchamgam K, Perakis G (2019b) High-low promotion policies for peak end demand models. Working paper, MIT, Cambridge, MA.Google Scholar
  • DARPA(2018) Darpa announces $2 billion campaign to develop next wave of ai technologies. Accessed September 20, 2019, www.darpa.mil/news-events/2018-09-07.Google Scholar
  • Davenport TH (2013) Analytics 3.0. Harvard Bus. Rev. 91(12):64.Google Scholar
  • Dawes RM (1979) The robust beauty of improper linear models in decision making. Amer. Psych. 34(7):571.CrossrefGoogle Scholar
  • Diamond A (2013) Executive functions. Annual Rev. Psych. 64:135–168.CrossrefGoogle Scholar
  • Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych. General 144(1):114.CrossrefGoogle Scholar
  • Dietvorst BJ, Simmons JP, Massey C (2016) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.Google Scholar
  • European Economic and Social Committee (2017) Impact of digitalisation and the on-demand economy on labour markets and the consequences for employment and industrial relations. Technical report, European Economic and Social Committee, Brussels, Belgium.Google Scholar
  • Farias VF, Li AA (2019) Learning preferences with side information. Management Sci. 65(7):3131–3149.LinkGoogle Scholar
  • Farias VF, Jagabathula S, Shah D (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.LinkGoogle Scholar
  • Felten E, Raj M, Seamans RC (2019) The effect of artificial intelligence on human labor: An ability-based approach. Working paper, Princeton University, Princeton, NJ.Google Scholar
  • Ferreira KJ, Lee BHA, Simchi-Levi D (2015) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.LinkGoogle Scholar
  • Frei P, Poulsen AH, Johansen C, Olsen JH, Steding-Jessen M, Schuz J (2011) Use of mobile phones and risk of brain tumours: Update of Danish cohort study. BMJ 343:d6387.Google Scholar
  • Frick W (2015) Here’s why people trust human judgment over algorithms. Harvard Bus. Rev. (February 27), https://hbr.org/2015/02/heres-why-people-trust-human-judgment-over-algorithms.Google Scholar
  • Good BM, Su AI (2013) Crowdsourcing for bioinformatics. Bioinformatics 29(16):1925–1933.CrossrefGoogle Scholar
  • Gopalkrishnan V, Steier D, Lewis H, Guszcza J, Lucker J (2013) Big data 2.0: New business strategies from big data. Deloitte Rev. 12:54–69.Google Scholar
  • GSMA (2018) The data value chain. Accessed September, 20, 2019, www.gsma.com/publicpolicy/wp-content/uploads/2018/06/GSMA_Data_Value_Chain_June_2018.pdf.Google Scholar
  • Gurkan H, de Véricourt F (2020) Contracting, pricing, and data collection under the AI flywheel effect. Working paper, ESMT, Berlin.Google Scholar
  • Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intelligent Systems 24(2):8–12.Google Scholar
  • Harrell E (2016) Managers shouldn’t fear algorithm-based decision making. Accessed September 5, 2020, hbr.org/2016/09/managers-shouldnt-fear-algorithm-based-decision-making.Google Scholar
  • Harsha P, Subramanian S, Uichanco J (2019) Dynamic pricing of omnichannel inventories. Manufacturing Service Oper. Management 21:47–65.LinkGoogle Scholar
  • Herman B (2017) The promise and peril of human evaluation for model interpretability. Preprint, submitted November 20, https://arxiv.org/abs/1711.07414.Google Scholar
  • Hoffman M, Kahn LB, Li D (2017) Discretion in hiring. Quart. J. Econom. 133(2):765–800.Google Scholar
  • Holzinger A (2016) Interactive machine learning for health informatics: When do we need the human-in-the-loop? Brain Inform. 3(2):119–131.Google Scholar
  • Holzinger A, Biemann C, Pattichis CS, Kell DB (2017) What do we need to build explainable AI systems for the medical domain? Preprint, submitted December 28, https://arxiv.org/abs/1712.09923.Google Scholar
  • Isaac WS (2017) Hope, hype, and fear: The promise and potential pitfalls of artificial intelligence in criminal justice. Ohio State J. Crimimal Law 15(2):543–558.Google Scholar
  • Johnson M (2017) Providing gender-specific translations in google translate. Accessed August 10, 2020, ai.googleblog.com/2018/12/providing-gender-specific-translations.html.Google Scholar
  • Katz M (2017) Welcome to the era of the AI coworker. Accessed Septyember 20, 2019, www.wired.com/story/welcome-to-the-era-of-the-ai-coworker.Google Scholar
  • Kim B (2015) Interactive and interpretable machine learning models for human machine collaboration. PhD thesis, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Knox WB, Stone P (2012) Reinforcement learning from human reward: Discounting in episodic tasks. 2012 IEEE RO-MAN: 21st IEEE Internat. Sympos. Robot Human Interactive Communication (IEEE, New York), 878–885.Google Scholar
  • Laureiro-Martínez D, Brusoni S (2018) Cognitive flexibility and adaptive decision-making: Evidence from a laboratory study of expert decision makers. Strategic Management J. 39(4):1031–1058.Google Scholar
  • Lipton ZC (2016) The mythos of model interpretability. Preprint, submitted June 10, https://arxiv.org/abs/1606.03490.Google Scholar
  • Logg JM, Minson JA, Moore DA (2019) Algorithm appreciation: People prefer algorithmic to human judgment. Organ. Behav. Human Decision Processes 151:90–103.Google Scholar
  • Mahmoudi M, Caracciolo G, Safavi-Sohi R, Poustchi H, Li AA, Vasighi M, Chiozzi RZ, et al.. (2017) Multi-nanoparticle protein corona characterization of human plasma and machine learning enable accurate identification and discrimination of cancers at early stages. Working paper, Harvard Medical School, Cambridge, MA.Google Scholar
  • Marcus G (2018) Deep learning: A critical appraisal. Preprint, submitted January 2, https://arxiv.org/abs/1801.00631.Google Scholar
  • Mayer-Schönberger V, Ingelsson E (2018) Big data and medicine: A big deal? J. Internal Medicine 283(5):419–429.Google Scholar
  • Meehl PE (1954) Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. University of Minnesota Press, Minneapolis.Google Scholar
  • Mills K (2017) ‘Racist’ soap dispenser refuses to help dark-skinned man wash his hands: But Twitter blames ‘technology’. Accessed August 10, 2020, www.mirror.co.uk/news/world-news/racist-soap-dispenser-refuses-help-11004385.Google Scholar
  • Mims C (2017) Without humans, artificial intelligence is still pretty stupid. Wall Street Journal, (November 12), www.wsj.com/articles/without-humans-artificial-intelligence-is-still-pretty-stupid-1510488000.Google Scholar
  • Misic V, Perakis G (2020) Data analytics in operations management: A review. Manufacturing Service Oper. Management 22(1):158–169.Google Scholar
  • Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453.CrossrefGoogle Scholar
  • Pachal P (2015) Google photos identified two black people as ‘gorillas’. Accessed August 10, 2020, https://mashable.com/2015/07/01/google-photos-black-people-gorillas/.Google Scholar
  • Papenfuss M (2017) Woman in China says colleague’s face was able to unlock her Iphone X. Accessed August 10, 2020, www.huffpostbrasil.com/entry/iphone-face-recognition-double_us_5a332cbce4b0ff955ad17d50.Google Scholar
  • Perakis G, Singhvi D, Skali-Lami O (2019) Xstrees: A tree sampling framework for interpretable tree ensembles. Working paper, MIT, Cambridge, MA.Google Scholar
  • Prates MO, Avelar PH, Lamb LC (2020) Assessing gender bias in machine translation: A case study with Google translate. Neural Comput. Appl. 32:6363–6638.CrossrefGoogle Scholar
  • Rani P, Liu C, Sarkar N, Vanman E (2006) An empirical study of machine learning techniques for affect recognition in human–robot interaction. PAA Pattern Anal. Appl. 9(1):58–69.CrossrefGoogle Scholar
  • Rath S, Rajaram K (2018) Staff planning for hospitals with cost estimation and optimization. Working paper, University of North Carolina, Chapel Hill, NC.Google Scholar
  • Rath S, Rajaram K, Mahajan A (2017) Integrated anesthesiologist and room scheduling for surgeries: Methodology and application. Oper. Res. 65(6):1460–1478.LinkGoogle Scholar
  • Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you? Explaining the predictions of any classifier. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, (Association for Computing Machinery, New York), 1135–1144.Google Scholar
  • Richardson R, Schultz JKC (2019) Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. New York University Law Review Online, www.nyulawreview.org/online-features/dirty-data-bad-predictions-how-civil-rights-violations-impact-police-data-predictive-policing-systems-and-justice/.Google Scholar
  • Ridgeway G, Madigan D, Richardson T, O’Kane J (1998) Interpretable boosted naïve bayes classification. Proc. 4th Internat. Conf. Knowledge Discovery and Data Mining (Association for the Advancement of Artificial Intelligence, Palo Alto, CA), 101–104.Google Scholar
  • Sarikaya R (2019) How Alexa learns. Accessed September 20, 2019, blogs.scientificamerican.com/observations/how-alexa-learns/.Google Scholar
  • Sparks ER, Venkataraman S, Kaftan T, Franklin MJ, Recht B (2017) Keystoneml: Optimizing pipelines for large-scale advanced analytics. 2017 IEEE 33rd Internat. Conf. Data Engineering (ICDE) (IEEE, New York), 535–546.Google Scholar
  • Stoffel E, Becker AS, Wurnig MC, Marcon M, Ghafoor S, Berger N, Boss A (2018) Distinction between phyllodes tumor and fibroadenoma in breast ultrasound using deep learning image analysis. Eur. J. Radiology Open 5:165–170.Google Scholar
  • Strausz R (2017) A theory of crowdfunding: A mechanism design approach with demand uncertainty and moral hazard. Amer. Econom. Rev. 107(6):1430–1476.CrossrefGoogle Scholar
  • Sun C, Shrivastava A, Singh S, Gupta A (2017) Revisiting unreasonable effectiveness of data in deep learning era. Preprint, submitted July 10, https://arxiv.org/abs/1707.02968.Google Scholar
  • Thomas LD, Leiponen A (2016) Big data commercialization. IEEE Engrg. Management Rev. 44(2):74–90.CrossrefGoogle Scholar
  • Thomaz AL, Breazeal C (2008) Teachable robots: Understanding human teaching behavior to build more effective robot learners. Artificial Intelligence 172(6–7):716–737.CrossrefGoogle Scholar
  • Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Royal Statist. Soc. B 58(1):267–288.CrossrefGoogle Scholar
  • Train KE (2009) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Vielma JP (2015) Mixed integer linear programming formulation techniques. SIAM Rev. 57(1):3–57.CrossrefGoogle Scholar
  • Wang Y, Kosinski M (2018) Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. J. Personality Soc. Psych. 114(2):246.CrossrefGoogle Scholar
  • Warren M (2016) The cure for cancer is data, mountains of data. Accessed September 20, 2019, https://www.wired.com/2016/10/eric-schadt-biodata-genomics-medical-research/.Google Scholar
  • Wilson HJ, Daugherty PR (2018) Collaborative intelligence: Humans and AI are joining forces. Harvard Bus. Rev. 96(4):114–123.Google Scholar
  • Xin D, Ma L, Song S, Parameswaran A (2018a) How developers iterate on machine learning workflows—A survey of the applied machine learning literature. Preprint, submitted March 27, https://arxiv.org/abs/1803.10311.Google Scholar
  • Xin D, Ma L, Liu J, Macke S, Song S, Parameswaran A (2018b) Accelerating human-in-the-loop machine learning: Challenges and opportunities. Proc. 2nd Workshop Data Management End-To-End Machine Learn. (Association for Computing Machinery, New York), 1–4.Google Scholar
  • Xin D, Macke S, Ma L, Liu J, Song S, Parameswaran A (2018c) Helix: Holistic optimization for accelerating iterative machine learning. Proc. VLDB Endowment 12(4):446–460.CrossrefGoogle Scholar
  • Yeomans M, Shah A, Mullainathan S, Kleinberg J (2017) Making sense of recommendations. J. Behav. Decision Making 32(4):403–414.Google Scholar
  • Zenteno AC, Carnes T, Levi R, Daily BJ, Dunn PF (2016) Systematic OR block allocation at a large academic medical center. Ann. Surg. 264(6):973–981.CrossrefGoogle Scholar
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