To augment human capacity—Artificial intelligence evolution through causal layered analysis

Farrow, Elissa

doi:10.1016/j.futures.2019.02.022

Cited by 38 publications

(39 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Husain ( 2017 , p. 169) suggests that “today, our sense of identity is…intrinsically tied up in our ability to produce economic output”. AI is designed to achieve some form of advantage, more often connected to power, position or economy, for organisational shareholders (Farrow 2019 ). AI replacing human workers brings out strong feelings, that require a compassionate and authentic response in the adaptation approach.…”

Section: Discussion: Key Findings and Implications For Organisationalmentioning

confidence: 99%

“…In Australian organisational settings, ‘mindset’ theory has been a regular part of organisational cultural and professional development since Dweck ( 2017 ) first released her book ‘ Mindset: Changing the way you think to fulfil your potential ’ in 2006. The educational components included preliminary information on the genealogy of AI technologies (Farrow 2019 ), current developments and the theoretical base of futures research, and Reason and Bradbury’s ( 2008 ) three-layered model. To set up the data collection processes, example traits of fixed and growth mindset were provided to all participants ( Dweck 2017 ; NeuroLeadership Institute 2018 ; Wilson 2014 ): Fixed Mindset—I’m either good at it, or I’m not; When I’m frustrated, I give up; I don’t like to be challenged; When I fail, I’m no good; Tell me I’m smart; My physical response is congruent with my attitude (down); If you succeed, I feel threatened; My abilities determine everything.…”

Section: The Workhop Processmentioning

confidence: 99%

See 1 more Smart Citation

Mindset matters: how mindset affects the ability of staff to anticipate and adapt to Artificial Intelligence (AI) future scenarios in organisational settings

Farrow

2020

AI & Soc

Self Cite

View full text Add to dashboard Cite

Any first step in organisational adaptation starts with individuals’ responses and willingness (or otherwise) to change an aspect of themselves given the transcontextual settings in which they are operating (Bateson in Small arcs of larger circles: framing through other patterns, Triarchy Press, Axminster, 2018). This research explores the implications for organisational adaptation strategies when Artificial Intelligence (AI) is being embedded into the ecology of the organisation, and when employees have a dominant fixed or growth mindset (Dweck in Mindset: changing the way you think to fulfil your potential. Robinson, London, 2017). Research participants were supplied with a single scenario based in 2030, where—as a result of Artificial Intelligence technology implementation—employees were going to be displaced. Using Torbert’s (Organizational wisdom and executive courage, New Lexington Press, San Francisco, 1998) ‘first, second and third person’ research theory, participants were asked to independently review their thoughts, sense, and image of the future from a fixed mindset position (considered to be the worst case), then from a growth mindset perspective (best case), and then do the same collectively. Five key findings are outlined which support the principle that having a growth mindset is a key component of adaptive capacity and futures literacy. The five key findings conclude that AI adaptation processes need to include compassion and authenticity, embodiment, fundamental needs and motivations, mutual learning and considering what lies beyond the edges of the organisation (Bateson in Small arcs of larger circles: framing through other patterns, Triarchy Press, Axminster, 2018).

show abstract

Section: Discussion: Key Findings and Implications For Organisationalmentioning

confidence: 99%

Section: The Workhop Processmentioning

confidence: 99%

Mindset matters: how mindset affects the ability of staff to anticipate and adapt to Artificial Intelligence (AI) future scenarios in organisational settings

Farrow

2020

AI & Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bu temel strateji ışığında e-öğrenme oyunlarının daha faydalı içerik sunumu gerçekleştirdiğini iddia eden çalışmalar e-oyunların farklı yenilikçi özelliklerini vurgulamaktadır. Kinect (Orona, Maldonado ve Martinez, 2016), sanal gerçeklik (Ke, Lee ve Xu, 2016) ve zenginleştirilmiş gerçeklik (Techakosit ve Wannapiroon, 2015), MMORPG (Hou, 2012;Souza, Silva ve Roazzi, 2010) ve yapay zeka destekli etkileşimli oyun sistemleri (Farrow, 2019) gibi gelişmeler günümüzün en gelişmiş yenilikçi e-oyun özellikleri olarak öne çıkmaktadır. Bu yenilikçi özellikler yakınsak yeni medya destekli aygıtlarla harmanlanmış olarak sunulabilmekte ve e-öğrenme alanında devrimsel gelişmelere kapı aralamaktadır.…”

Section: E-öğrenme Oyunlarıunclassified

E-Öğrenme Oyunlarinda Algilanan Haz Ve Bi̇lgi̇ Geli̇şi̇mi̇ni̇n Değerlendi̇ri̇lmesi̇: Egameflow Ölçeği̇ni̇n Türkçe’ye Uyarlanmasi

Yanik

Batu

2019

Eğitim Teknolojisi Kuram Ve Uygulama

View full text Add to dashboard Cite

The basic understanding of e-learning games is to enable the student to reach both his / her participation and learning objectives with a correct flow design. At this point, the existence of a scale that effectively measures the perceived flow created by e-learning games and the knowledge improvement is very important. Thus, game designers can further integrate knowledge improvement factors with the game through the student perspective, while educators can successfully test the quality of the game's knowledge improvement. However, since the scales used for e-learning games are generally developed on the basis of commercial games, they cannot fully explain the pleasure and knowledge improvement in games-oriented games. The aim of this study is to adapt the EGameFlow Scale, which responds to the quality of the students' perceived enjoyment and knowledge improvement in e-learning games, and to test its validity and reliability. Explanatory Factor Analysis and Confirmatory Factor Analysis were applied for the construct validity, and the correlation of EGameFlow Scale and Achievement Goals Orientation Scale were calculated for compatibility validity. EGameFlow Scale reliability was determined by internal consistency, test split, and test-retest. As a result of Explanatory Factor Analysis conducted, a total of 21 items were extracted from the scale and all items were collected under 2 factors. The resulting new 2-factor construct explained 48.72% of the total variance. According to research findings, the Turkish form of EGameFlow Scale is an adequate tool for measuring the perceived enjoyment and the knowledge improvement of e-learning games.

show abstract

“…It has been around since the 1950s, specifically since 1956 during a Dartmouth Summer Research Project on Artificial Intelligence, as identified by different scholars, in which AI became recognized as an academic field. A research agenda for the next twenty years was established (Bevolo and Amati 2020; Farrow 2019). It has also been recognized that there has been an irregular development of AI since that date, having ups and downs, probably because of hardware limitations and shifting military, academic and business interests (Duan et al 2019).…”

mentioning

confidence: 99%

“…Different papers agree that one of the main complexities is the lack of an agreed and clearly limited definition of what AI means. Some authors (Duan et al 2019; Farrow 2019; van Belkom 2020) consider AI an umbrella concept that for the last fifty years has been extended and limited to include or exclude related concepts such as data science, data mining, expert systems, among others. Duan and others (2019) identified twenty-five different concepts related to AI and Big Data that add complexity since there is no common language among different disciplines.…”

mentioning

confidence: 99%

Guest Editorial: On “Big Data, AI, and Futures Studies”

Rincón¹

2020

World Futures Review

View full text Add to dashboard Cite

To augment human capacity—Artificial intelligence evolution through causal layered analysis

Cited by 38 publications

References 21 publications

Mindset matters: how mindset affects the ability of staff to anticipate and adapt to Artificial Intelligence (AI) future scenarios in organisational settings

Mindset matters: how mindset affects the ability of staff to anticipate and adapt to Artificial Intelligence (AI) future scenarios in organisational settings

E-Öğrenme Oyunlarinda Algilanan Haz Ve Bi̇lgi̇ Geli̇şi̇mi̇ni̇n Değerlendi̇ri̇lmesi̇: Egameflow Ölçeği̇ni̇n Türkçe’ye Uyarlanmasi

Guest Editorial: On “Big Data, AI, and Futures Studies”

Contact Info

Product

Resources

About