Mitigation and impact management

Mareddy, Anji Reddy

doi:10.1016/b978-0-12-811139-0.00011-6

Cited by 2 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ex-ante (Pre-deployment) Environmental [159,277] Social and Fiscal [20,96,287] Ex-post (Post-deployment) Temporal and long-term effects [78,206] Failure and misuse [82,105] Strategic [193,264] Fig. 7.…”

Section: Safety Through Impact Assessmentmentioning

confidence: 99%

“…Environmental impact assessment (EIA): Both deductive and inductive causal inference have long been used in EIA [159,277]. While with a deductive approach, a hypothesis about a causal relationship is formed and tested, using an inductive approach, data are collected from observations, and a causal relationship is inferred from the instances.…”

Section: Ex-ante Impact Assessmentmentioning

confidence: 99%

“…While with a deductive approach, a hypothesis about a causal relationship is formed and tested, using an inductive approach, data are collected from observations, and a causal relationship is inferred from the instances. Causal networks have long been used for EIA [159,277] since they bring both network (multiple interaction pathways between environment and various activities) and cause-effect (various activities affecting the elements of the environment) logic to the analysis. Causal networks allow an analysis of impacts through sequences of interactions [159]--also referred to as sequence diagrams [36].…”

Section: Ex-ante Impact Assessmentmentioning

confidence: 99%

“…Causal networks have long been used for EIA [159,277] since they bring both network (multiple interaction pathways between environment and various activities) and cause-effect (various activities affecting the elements of the environment) logic to the analysis. Causal networks allow an analysis of impacts through sequences of interactions [159]--also referred to as sequence diagrams [36]. Causal networks (structural causal models) have helped find indirect impacts on multiple levels [36].…”

Section: Ex-ante Impact Assessmentmentioning

confidence: 99%

See 3 more Smart Citations

A Review of the Role of Causality in Developing Trustworthy AI Systems

Ganguly¹,

Fazlija²,

Badar³

et al. 2023

Preprint

View full text Add to dashboard Cite

State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.

show abstract

Section: Safety Through Impact Assessmentmentioning

confidence: 99%

Section: Ex-ante Impact Assessmentmentioning

confidence: 99%

Section: Ex-ante Impact Assessmentmentioning

confidence: 99%

Section: Ex-ante Impact Assessmentmentioning

confidence: 99%

See 2 more Smart Citations

A Review of the Role of Causality in Developing Trustworthy AI Systems

Ganguly¹,

Fazlija²,

Badar³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Soil contamination can be defined as the accumulation in the soil of persistent harmful substances, chemical compounds, radioactive waste, salts, pathogens and others that harm biological systems (OKRENT, 1999;MAREDDY, SHAH AND DAVERGAVE, 2017).…”

Section: Contamination Problemmentioning

confidence: 99%

The trend of bioremediation as an effective technology in soil decontamination

Bones¹,

Flach²,

Rosa³

2023

A Look at Development

View full text Add to dashboard Cite

This paper reviews the different techniques of bioremediation as a technology that uses living organisms, such as bacteria, fungi, and plants, to remove, degrade or transform contaminants present in soil, water, or air into less harmful substances. The research highlights the advantages of bioremediation for the mitigation of anthropogenic impacts on the environment, especially those caused by the use of herbicides and insecticides in agricultural practices. In addition, bioremediation can degrade a wide variety of contaminants, including hydrocarbons, heavy metals, pesticides, and toxic organic compounds, which can accumulate in the medium or be leached to contaminate water sources. In this context, it is emphasized that bioremediation can be applied directly to the contaminated site, reducing the need for the removal and transportation of large amounts of contaminated soil. This results in lower environmental impact and lower cost compared to other decontamination techniques. Another advantage of bioremediation is the integration of degraded contaminants into biogeochemical cycles, which contributes to the recovery of soil fertility and water quality. The technique also preserves biodiversity and ecosystem services, which can be affected by other decontamination techniques that alter the physical and chemical characteristics of the environment. However, there are also challenges and limitations of bioremediation, such as the possibility of partial degradation of the target compound and the formation of a more toxic or persistent by-product. This can occur due to the dependence on favorable environmental conditions, such as nutrients, oxygen, pH, composition, concentration, and bioavailability of contaminants, as well as the physical and chemical characteristics of soil and water. In addition, bioremediation is a promising tool for promoting the health and quality of life of populations affected by environmental contamination, but it requires an in-depth knowledge of the processes involved and a careful assessment of the risks and benefits.

show abstract

Mitigation and impact management

Cited by 2 publications

References 0 publications

A Review of the Role of Causality in Developing Trustworthy AI Systems

A Review of the Role of Causality in Developing Trustworthy AI Systems

The trend of bioremediation as an effective technology in soil decontamination

Contact Info

Product

Resources

About