Search results

The Australian Government recognises an obligation to ensure that Aboriginal and Torres Strait Islanders (Australia’s First Peoples) are included in the integration of genomics into the healthcare system. First Peoples inclusion in this area requires going beyond general principles for First Peoples health research. This extra need exists for historical reasons as well as the need to maintain connections between patients, participants and communities and between the bio-specimens and data contributing to the resources underpinning genomics. The National Centre for Indigenous Genomics (NCIG) at the Australian National University in Canberra, Australia, has developed a framework that addresses these requirements through its dedicated First Peoples governance and enduring community engagement processes relating to stored heritage materials, data management and culturally agreed terms for collection preservation and potential use into the future. This chapter incorporates a First Peoples perspective on the NCIG by describing the practical application of ‘doing the right thing’, proceeding at ‘the pace of trust’, obtaining informed consent as part of enduring relationships, acknowledging cultural perspectives, understanding diversity of views and cultural practices within and between communities and respecting the need for community ownership and self-determined mobilisation of First Peoples involvement with research. This culturally appropriate methodology has been developed in partnership with individuals, family groups and community leaders, who are directly involved in genomic research. It provides a model for First Peoples to play an invested and sustaining role in the future development of genome science and precision medicine.

The focus of the chapter is on disputes around corporate social responsibility (CSR) in the fossil fuel industry and how media and social networking technologies are deployed in a virtual war between oil corporations and dissident, activist and protest groups.

Communications by BP, Shell, and their opponents in this virtual war are compared, especially in relation to the creative use of the internet, digital technologies, and social media. Through a case study approach, the chapter shows how communications often center on contested notions of CSR and claims by the oil giants about their environmental impact, which opponents dismiss as “greenwashing.” The various techniques deployed by both sides in this wide-ranging “PR war” are explored and contrasted in detail.

The findings for each case study reveal the diverse, complex, and changing nature of the relationship between the oil industry and its critics. The chapter concludes by arguing that if CSR is seen as “greenwashing” by the public, it is only likely to fuel widespread skepticism of the oil and gas sector and of corporate claims about the environment more generally.

The research offers a snapshot of online and social media campaigns and PR strategies and tactics within the oil and gas industry rather than empirically grounded set of findings that can be easily applied to other fields.

Practical implications include attention to inflated or understated claims and the use of citizen testimony and humor to puncture CSR “rhetoric.” There is consideration of use of digital technologies by activists and attention to the way public debates and consultations are conducted. The need for a more respectful engagement with local communities by all parties engaging in public relations is underlined.

The chapter applies the concept of “asymmetrical warfare” from conflict studies within the media and communications tradition to provide a fresh revaluation of the term “PR war,” It offers a rare focus on online efforts by activist to subvert CSR-related branding, marketing, and communications. Discussion of the use of parody alongside factual and emotional arguments to challenge corporate hegemony also provides revealing insights.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.